Master of Mathematical Engineering (Leuven)

CQ Master of Mathematical Engineering (Leuven)

Opleiding

What can you find on this webpage?

Our (future) students can find the official study programme and other useful info here.

You can find information about admission requirements, further studies and more practical info such as ECTS sheets, or a weekly timetable of the current academic year.

Are you a future student?

Be sure to first take a look at the page about the Master of Mathematical Engineering.

There you can find more info on:

- What’s the programme about?

- Starting profile

- Admission and application

- Future possibilities

- Why KU Leuven

- Contact

- ...

Toelatingsvoorwaarden

Master of Mathematical Engineering (Leuven)onderwijsaanbod.kuleuven.be/2024/opleidingen/e/SC_52357132.htm#activetab=voorwaarden

Doelstellingen

1. Competent in one or more scientific disciplines
- (1)Possesses specialized knowledge in the field of Mathematical Engineering:
o Design, analysis, implementation and use of mathematical models
o Numerical algorithms
In a context of simulation, identification, monitoring, control and optimization of industrial systems and knowledge systems.
- (2)Possesses specialized knowledge in two or more of the following application areas:
o Industrial process control
o Data mining
o Scientific computing and simulation
o Cryptography
- (3)Can creatively apply, expand, deepen and integrate knowledge of different fields of mathematical engineering.
- (4)Integrates the acquired knowledge into basic sciences and in a number of engineering disciplines and is capable of multidisciplinary thinking and acting.

2. Competent in conducting research
- (5)Can divide a complex realistic problem in sub-problems, and is able to structure these sub-problems into research questions and research strategies.
- (6)Can independently gather all the scientific information about a topic, assess its relevance and process the valuable aspects with attention to proper source indication.
- (7)Can establish, execute and adjust an independent research project about new technical and scientific methods.
- (8)Can gain new insights from generated results and discuss these insights critically.

3. Competent in designing
- (9)Can reformulate a design problem in specific design objectives.
- (10)Can design and implement mathematical techniques and algorithms in order to solve problems in application fields such as industrial process control, data mining, image processing, scientific computing and simulation, and cryptography.
- (11)Can design solutions for multidisciplinary problems, often with an open nature.
- (12)Controls the complexity of the design of mathematical techniques and algorithms by means of abstraction and structured thinking.
- (13)Can critically evaluate and report on design results.
- (14)Can handle the variability of the design process due to external circumstances or new insights.

4. A scientific approach
- (15)Has a systematic approach, critical attitude and understanding of the specificity of science and technology
- (16)Can critically observe current mathematical theories, models and methodologies in the context of engineering problems, and make a sound decision.
- (17)Can evaluate the efficiency and accuracy of methods.
- (18)Demonstrates academic integrity.
- (19)Is able to independently keep up with developments in their field.

5. Basic intellectual skills
- (20)Can independently reflect critically and constructively on their own thinking, decision making and actions.
- (21)Can reflect critically and objectively on developments in their own field of engineering.
- (22)Can objectively consider positive and negative aspects of a solution, and select the most realistic, efficient and effective solution for a specific situation.
- (23)Can formulate a reasoned opinion in the case of incomplete or irrelevant information.

6. Competent in co-operating and communicating
- (24)Can communicate orally and in writing about his or her research and solutions in Dutch and English with colleagues and stakeholders.
- (25)Can work on a project basis: takes into account the limited resources (computing time, memory usage,...), can deal with deadlines, possesses pragmatism, can apply the basic techniques of project management.
- (26)Can efficiently work in groups and carry team roles.

7. Takes account of the temporal and social context
- (27)Is aware of the role played by mathematical processes in a complex and changing high-tech society (legal, economic, sociological, political and technical-industrial context).
- (28)Is aware of their social, ethical and environmental responsibility as a mathematical engineer and acts accordingly.

The graduated master:

  • During the practice of the engineering profession, is guided by his or her scientific and technical knowledge.
  • Has an engineering attitude that enables him or her to formulate solutions to complex problems, taking into account relevant constraints of an economic, legal, social, ... nature.
  • Is aware of his or her social and ethical responsibility and can act accordingly.
  • Has a willingness for open communication and cooperation, both with engineers within and outside the discipline, and with other actors in the professional field.
  • Has insight into the broader role that engineers play in society.
  • Shows willingness to keep abreast of new scientific and technical evolutions, and to approach them with a critical mind.

Educational quality of the study programme

Here you can find an overview of the results of the COBRA internal quality assurance method.

Educational quality at study programme level

Blueprint
Bestand PDF document Blueprint_MA_Engineering Science_Mathematical Engineering.pdf

COBRA 2019-2023
Bestand PDF document COBRA-fiche_MA_Mathematical engineering_2022-2023 WIT-2023.pdf

COBRA 2015-2019
Bestand PDF document COBRA-report_MA_Mathematical Engineering.pdf

Educational quality at university level

  • Consult the documents on educational quality available at university level.

More information?

SC Master of Mathematical Engineering (Leuven)

programma

This program can be adapted according to the previous knowledge of the student.

All subgroups are compulsory.

printECTS33.xsl

ECTS Innovation Management and Strategy (B-KUL-D0H36A)

6 ECTS English 39 First termFirst term Cannot be taken as part of an examination contract

Aims

Upon completion of this course, the student is able to:

• Define, clarify and understand major concepts and topics which constitute the specific nature of innovation dynamics/innovation systems.
• Define and clarify concepts  and models (rationale, ingredients, implications) relevant for defining  and implementing an innovation strategy (on the level of the firm)
• Define and clarify concepts and models (rationale, ingredients, implications) relevant for organising new product development efforts (project level)

Previous knowledge

No specific prerequisites.

Is included in these courses of study

Onderwijsleeractiviteiten

Innovation Management and Strategy (B-KUL-D0H36a)

6 ECTS : Lecture 39 First termFirst term

Content

Part 1: Technology and innovation dynamics/systems: Key concepts and Insights
Part 2: Defining and implementing an innovation strategy (at the level of the firm)
Part 3: Management of New Product Development processes (Project level)

Each part provides the students with a grounded and scientific approach towards important aspects of the innovation process. As a consequence, major scientific as well as application-oriented articles are provided as reading materials for each module.

The first part highlights the disciplinary roots or origins of the innovation process. More specifically, we highlight economic studies of the innovation process. These studies delve deeper into the work and insights of Joseph Schumpeter on the role of entrepreneurs and established companies and market pull and technology push dynamics. In addition we will elaborate insights on the level of innovation systems including the relevancy of (support) policies (e.g. patent systems) as well as the role of research centers and universities within such systems.

The second part develops models of the innovation process and examines the strategic management of technology and innovation on the level of the firm. Both defining an innovation strategy and implementing the innovation strategy by means of concepts and practices such as the development of technology portfolios (including selection criteria for innovation projects and programs) and technology roadmaps will be discussed. Major issues in organising the effective implementation of innovation strategies will be introduced (e.g. Organisational Ambidexterity, Venturing,.. .). We will also look at the nature and relevance of alliances and cooperation for the innovative performance of the firm.

The third part discusses the management of day-to-day operations in innovation environments. We discuss the following themes: (1) organising innovation activities and new product development projects, (2) critical success factors in managing innovation projects, (3) the concept of project performance in innovative settings, (4) techniques and approaches to support project management in innovative environments, and (5) the management of innovative teams and professionals.

Course material

Used Course Material
Handbook: Innovation Management and Strategy, Van Looy. McGraw Hill, 2016. (ISBN: 9781308882987)
Articles and literature
Slides, transparencies, courseware

Toledo
* Toledo is used for this learning activity to share readings, lecture slides, etc.

Format: more information

Students acquire in-depth insights in the management of innovation and technology in a course that combines traditional lectures and a group assignment.

For the group assignment students write a paper and give a poster presentation. Students demonstrate their ability to analyse and understand innovation dynamics. Given the scope of the course, topics can be situated at 3 different levels: innovation systems and policies, innovation strategies of firms and innovative products/projects (including business models).

Evaluatieactiviteiten

Evaluation: Innovation Management and Strategy (B-KUL-D2H36a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Paper/Project, Presentation
Type of questions : Open questions
Learning material : None

Explanation

Features of the evaluation

* The written closed book exam assesses the extent to which the student has internalised the insights from the readings and lectures and is able to diagnose innovation dynamics, develop relevant arguments and understands consequences and implications of proposed actions.

* The paper and presentation should reflect that the student is able to analyse and understand the specific nature of their topic, to compare/situate the topic within the relevant (scientific) literature and to arrive at an assessment in terms of appropriateness. This is a group assignment.

* The paper and presentation are group assignments in teams of 4-6 people.

* For the paper the term of deliverance and deadline will be determined by the lecturer and communicated via Toledo. The deadline will be situated before the start of the examination period at the end of the semester.

* The final presentation date will be set by the lecturer and communicated via Toledo. The presentations will take place before the start of the examination period; at the end of the semester.

Determination of final grades

* The grades are determined by the lecturer as communicated via Toledo and stated in the examination schedule. The result is calculated and communicated as a number on a scale of 20.

* The final grade is a weighted score and consists of the following components: 60% on a written closed book exam; 40% on the written paper and presentation

* The grade for the paper is only taken into account if the student succeeds in the final exam.

* If the student does not participate in the written exam, the final grade of the course will be NA (not taken) for the whole course.

* If the set deadline for the paper was not respected, the grade for that respective part will be a 0-grade, unless agreed otherwise by the lecturer. Changes in deadlines can only be considered in case of unexpected, severe, circumstances.

* If the student did not participate in the elaboration of the paper, the grades for the paper and presentation will be a 0-grade.

Second examination opportunity

* The features of the evaluation and determination of grades are similar to those of the first examination opportunity, as described above.

* The student retakes that part of the evaluation (written closed book exam and/or paper and  presentation) for which he did not pass. The grade obtained at the first exam opportunity for the part the student did pass, will be transferred to the second exam opportunity.

* If students did not pass for the paper and presentation (and did not pass overall), a tailor made trajectory (individual) for the paper can be considered/allowed.

ECTS ICT Service Management (B-KUL-D0I69A)

6 ECTS English 39 Second termSecond term

Aims

Upon completion of this course, the student has a fundamental understanding of the management of ICT-based Services. The student is confronted with the underlying theory and best practices, extensively illustrated by means of case studies. In particular the student is able to:

  • define precisely ICT-based Services from a demand as well as support perspective, and understand the positioning of Services in Business Processes
  • define and implement an overal ICT-based Service Catalogue for an organisation
  • apply relevant costing policies to ICT-based Services
  • make the business case for the development of new and innovative services (including cost-benefit analysis)
  • decide on (out)sourcing issues in ICT-based Services
  • evaluate the applicability of Service Management Frameworks (ITIL, ISO20000, CMMI and COBIT) for Service Delivery as well as Support

Previous knowledge

At the beginning of this course, the student should possess a basis knowledge of Information Systems.
Furthermore, knowledge of basic micro-economical techniques is recommended.

Is included in these courses of study

Onderwijsleeractiviteiten

ICT Service Management (B-KUL-D0I69a)

6 ECTS : Lecture 39 Second termSecond term

Content

The topics of this course are:

Part I: IT Service management

* Introduction: IT in a business environment
* A framework for IT management
* IT managment standards and frameworks
* Services delivery management
* Financial managemement
* Operations management
* Services quality management
* Security management
* Supplier management
* Human resources management

Part II: IT governance

* Introduction: what is IT governance?
* The ISO 38500 standard
* The COBIT framework
* IT decision making
* IT spending
* Business-IT alignment

Course material

Mandatory course material: course book: The IT Management Essentials - Delivering business value, ISBN: 9789057187513

Toledo: in depth explanations and comments of parts of the course book, literature, slides.

Language of instruction: more information

De deelnemersgroep is sterk internationaal samengesteld

Format: more information

The course consists of:

1. Lectures where the topics are presented, elaborated and discussed.
2. Exercises where the topics are applied to specific situations. Exercises are prepared by the students and discussed during class.
3. Self-study, in particular the reading of research papers.

Evaluatieactiviteiten

Evaluation: ICT Service Management (B-KUL-D2I69a)

Type : Partial or continuous assessment with (final) exam during the examination period
Type of questions : Open questions
Learning material : Calculator

Explanation

The exam is oriented towards understanding and applying IT governance and management research, standards, frameworks, principles, processes and techniques.
The grades are determined by the lecturer as communicated via Toledo and stated in the examination schedule. The result is calculated and communicated as a whole number on a scale of 20.
The features of the evaluation and determination of grades are identical to those of the first examination opportunity, as described above.

ECTS Fundamental Concepts of Statistics (B-KUL-G0A17A)

6 ECTS English 26 First termFirst term Cannot be taken as part of an examination contract
N. |  Segers Johan (substitute)

Aims

Upon completion of this course, the student is able to

  • understand the basic ingredients of probability theory (e.g. sigma-algebra, sample space, probability measure) and to apply the definitions and properties in examples; understand the concepts of independence and conditional probability and to apply them and the laws on conditional probabilities (law of total probability, Bayes' rule) in applications (e.g. system reliability).
  • understand, describe, recognize and use several discrete and continuous random variables (both univariate as multivariate); describe and find the univariate and joint distribution of random variables (and transformations of random variables) using probability density functions, cumulative distribution functions and/or moment generating functions.
  • calculate and interpret characteristics of random variables (and transformations of random variables): expectations, variances and higher (central) moments directly, by using moment generating functions, by using conditional computing (e.ge. double expectation law) and/or by using the delta method (leading to approximate moments);  calculate and interpret quantiles directly; understand, describe and use the concept of statistical independence (between random variables) and conditional distributions in examples; calculate and interpret covariance and correlation of random variables and moments of order statistics.
  • find (sampling) distribution of some common sample statistics and calculate and interpret corresponding moments, probabilities and quantiles; understand, describe and use the concept of convergence (in probability and in distribution) and asymptotic normality of (transformed) random variables and some related properties and theorems (e.g. Chebyshev's inequality, Slutsky's theorem); understand and apply the law of large numbers and the central limit theorem in examples.
  • construct point estimators and calculate point estimates (using maximum likelihood method), to evaluate their goodness (calculating bias, variance, mean squared error) and to determine and describe desirable properties of the estimators (related to efficiency, consistency and sufficiency); construct and interpret confidence intervals (and describe and test some of the assumptions that are made).
  • describe and perform hypothesis tests, to compute p-values and probabilites of type I and type II errors, to determine the power of a test, to calculate the sample size needed for a given power and to construct likelihood ratio tests and Pearson's chi-squared tests; describe and perform some nonparametric tests; describe the bootstrapping technique and calculating bootstrap estimates of standard error.

Previous knowledge

A practical knowledge of basics of probability, descriptive statistics and applied statistical inference is assumed. The expectation should be to deepen out the foundations and the optimality of methods in statistics, knowing that the mathematical language is a basic tool.


Beginning conditions: Basic calculus (including the concepts of derivative and integration) and a course on statistics, both at undergraduate level.

Identical courses

G0A17B: Fundamental Concepts of Statistics

Is included in these courses of study

Onderwijsleeractiviteiten

Fundamental Concepts of Statistics (B-KUL-G0A17a)

6 ECTS : Lecture 26 First termFirst term
N. |  Segers Johan (substitute)

Content

The course consists of the following topics

1. Probability.

2. Random variables and distributions

3. Multivariate distributions

4. Transformation of random variables

5. Convergence of random variables

6. Sample statistics and limit theorems

7. Estimation

8. Confidence intervals

9. Hypothesis testing

10. Bootstrap

Course material

The course material consists of slides and on-line material provided by the teacher.  The book Mathematical Statistics and Data Analysis, J.A. Rice (Duxbury Press) is recommended literature as the course follows this book closely.

Background information can be found in several chapters of these other books:

  • Probability and Statistics, M.H. DeGroot and M.J. Schervish (Pearson Education)
  • Mathematical Statistics with Applications, D. Wackerly, W. Mendenhall and R.L. Scheaffer (Thomson Brooks/Cole)
  • A Course in Probability, N.A. Weiss (Pearson Education)

Format: more information

Next to the teaching in auditorium, on-line material such as weblectures and screencasts will be available. Q&A sessions will be provided weekly in class and on-line.

Is also included in other courses

G0A17B : Fundamental Concepts of Statistics

Evaluatieactiviteiten

Evaluation: Fundamental Concepts of Statistics (B-KUL-G2A17a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Multiple choice, Open questions
Learning material : None

Explanation

more information is provided on Toledo

Information about retaking exams

At the second examination opportunity, the bonus points (possibly obtained with the test) is no longer part of the evaluation.
The grade is entirely based on the final exam.

ECTS Statistical Software (B-KUL-G0A21A)

3 ECTS English 19 First termFirst term Cannot be taken as part of an examination contract

Aims

This course gives an introduction to the use of the statistical software languages SAS and R.

With the SAS software:
The student should be able to
- Write a SAS program
- Work with libraries and SAS help
- Work with basic SAS procedures
- Do some data handling by using the DATA step and Proc SQL
- Use ODS

With the R software:
The student should be able to
- Write an R script
- Work with data structures
- Create an R function
- Create graphs
- Use R functions to produce basic statistical results
- Install R packages and work with R help

Identical courses

G0A21B: Statistical Software
G00C3A: Statistische software

Is included in these courses of study

Onderwijsleeractiviteiten

Statistical Software (B-KUL-G0A21a)

3 ECTS : Lecture 19 First termFirst term

Content

R Studio

  • Introduction and preliminaries
  • Data Structures
  • Importing and exporting data
  • Writing your own function
  • Graphics with R
  • Basic functions in dplyr package
  • More on programming with R
  • Some data analysis with R
  • GGplot2

 

SAS programming

Part 1: SAS Programming 1: Essentials

  • Essentials
  • Accessing Data
  • Exploring and validating data
  • Preparing data
  • Analyzing and reporting on data
  • Exporting results
  • Using SQL in SAS

Part 2: SAS Programming 2: Data Manipulation Techniques

  • Controlling DATA step processing
  • Summarizing Data
  • Manipulating Data with functions
  • Creating custom formats
  • Combining tables
  • Processing repetitive code
  • Restructuring tables

 

 

Course material

Online teaching with Q&A sessions, via Toledo and Collaborate

  • R Lectures are recorded in advance and are available on Toledo. R notes are available on Toledo
  • SAS has to be studied by using the SAS e-learnings provided by SAS 
  • Discussion board on Toledo is available. 
  • Regularly a Q&A session is organized  

Is also included in other courses

G0A21B : Statistical Software

Evaluatieactiviteiten

Evaluation: Statistical Software (B-KUL-G2A21a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Oral
Learning material : Computer

Explanation

The evaluation consists of two pc exams on the campus.

1. SAS exam counts for 50 % of the final grade. The sas exam is a closed book, pc exam outside of the normal exam period.

2. R exam counts for 50 % of the final grade. This R exam is an open book pc exam in the normal exam period.

The details and consequences for students who do not participate in both exams can be found on Toledo.

Information about retaking exams

The features of the evaluation and determination of grades are similar to those of the first examination opportunity, as described above.

ECTS Computational Methods for Astrophysical Applications (B-KUL-G0B30A)

6 ECTS English 39 First termFirst term
Keppens Rony (coordinator) |  Keppens Rony |  Sundqvist Jon

Aims

The course starts with an introduction to common spatial and temporal discretization techniques to numerically solve sets of partial differential equations. Further on, the course treats various state-of-the-art numerical methods used in astrophysical computations. This encompasses basic shock-capturing schemes as employed in modern Computational Fluid Dynamics, common approaches for handling Radiative Transfer, and concrete gas dynamical applications with astrophysical counterparts. The main aim is to give insight in the advantages and disadvantages of the employed numerical techniques. The course will illustrate their typical use with examples which concentrate on stellar out-flows where the role and numerical treatment of radiative losses will be illustrated, but also touch on studies from solar physics, stellar atmospheres, astrophysical accretion disks and jets, pulsar winds, planetary nebulae, interacting stellar winds, supernovae . . . . The students will experiment with existing and/or self-written software, and gain hands-on insight in algorithms, their convergence rates, time step limitations, stability, .... The students will in the end be able to apply some of the schemes to selected test problems.

Previous knowledge

No other previous knowlegde is needed than that allowing to attend master level courses.  More specifically, students should have a basic knowledge of calculus, differential equations and general physics, as is provided in any bachelor programme in mathematics or physics.

Although there is no specific requirement on prior knowledge, it is certainly worthwhile to combine this master course with one of Plasma Physics of the Sun, Introduction to Plasma Dynamics, Space Weather, Radiative Processes, Waves and Instabilities, Stellar Atmospheres. A related, more analytically oriented, Bachelor course is ‘Mathematical introduction to Fluid Dynamics’.

Onderwijsleeractiviteiten

Computational Methods for Astrophysical Applications (B-KUL-G0B30a)

4 ECTS : Lecture 26 First termFirst term

Content

The course is organized in modules. The basic modules consist of lectures combined with (home assignment and group)  worksessions, and will cover:
 
1. Introduction
a. Developing numerical codes
   – Computer code development, programming techniques, code maintenance, optimization
   – Concepts of verification, validation, sensitivity analysis, error and uncertainty quantification
 b. Spatial and temporal discretization techniques.
   – Spatial discretizations: Basic concepts for discrete representations. Finite difference, finite element, and spectral methods. An application: solving a Sturm-Liouville model problem and handling boundary conditions (eigenoscillations of a planar stellar atmosphere).
   – Temporal discretizations: Explicit versus implicit time integration strategies. Semi-discretization, predictor-corrector and Runge-Kutta schemes.
 
2. Towards computational gas dynamics.
• The advection equation and handling discontinuous solutions numerically. Stability, diffusion, dispersion, and order of accuracy, demonstrated with linear advection problems. Extension to linear hyperbolic systems and solution of the Riemann problem. Nonlinear scalar equations and shocks: solving Burgers equation. Non-conservative versus conservative schemes.
• Isothermal hydrodynamics and basic stellar wind models. Governing equations, Rankine-Hugoniot conditions, Prandtl-Meyer shock relation. Rarefactions, integral curves and Riemann invariants. Application to transonic stellar winds: Parker solar wind solution, isothermal rotating transonic winds, shocked accretion flows.
 
3. Compressible gas dynamics and multi-dimensional applications.
• The Euler equations and finite volume methods. Conservative form, Rankine-Hugoniot shock relations, exact solution of the Riemann problem, Riemann invariants. Basic shock-capturing discretization methods: finite volume methods and the TVDLF algorithm.
Possible advanced topic: Roe solver. Godunov scheme for Euler equations, Approximate Riemann solver, Roe scheme, numerical tests.
• Extensions to multi-dimensional algorithms and example multi-dimensional stellar wind models. Example 2D Euler simulations, emphasizing stellar wind models for various evolutionary phases, for cool to massive stars. Extension to interacting wind models using optically thin radiative losses. Attention to failures of modern schemes that still plague 1D and multi-D Euler simulations.
 
4. Numerical radiative transfer.
• Basic radiative transfer. The governing equations of radiative transfer and the rate equations. Discretization, treatment of angle dependence (with angle quadrature), handling of frequencies and optical depths.
• Specific numerical treatments. Feautrier method, Lambda iteration, Multi-level iteration. Application to stellar winds which are dust or radiative driven.
 
5. Intro to Computational Magneto-Hydro-Dynamics.
• Introduction: the MHD model. Applicability, use in astrophysical context.
• Transmagnetosonic stellar winds and 1D MHD simulations. Weber-Davis MHD wind model, numerical simulations for solar and stellar rotating, magnetized winds, consequences for stellar rotational evolution. MHD shocks, Riemann problem tests.
 
A final module can be chosen depending on the interest of the students, linking to current research trends.

Course material

The lecture sheets are made available through Toledo. Additional course notes are provided online as well. Reference books are (students will not be required to purchase these, no book covers all topics):

  • Numerical Methods in astrophysics, Taylor & Francis 2007, Bodenheimer et al.
  • Advanced Magnetohydrodynamics, Cambridge University Press 2010, Goedbloed, Keppens, Poedts

Format: more information

Next to the lectures, students will either individually or in pair work out computerassignments, directly related to the topics covered. This will encompass both self-coding for a relevant toy problem and using advanced state-of-the-art software in a modern application. Part of these will be organized in joint computerclass sessions.

Computational Methods for Astrophysical Applications: Computerlab (B-KUL-G0B31a)

2 ECTS : Assignment 13 First termFirst term

Content

Using a combination of self-written and available software to solve selected astrophysical toy problems numerically. The idea is to gain insight in method limitations, as well as get acquainted with its inherent possibilities.

In a first part, the students will be asked to program their own solver.

In a second part, the students perform selected hydrodynamic simulations, and learn how to interpret and visualize their computational data.

Course material

During the second assigment, we make use of opensource modern computational codes, specifically the MPI-AMRVAC code, widely used in astrophysical applications.

Format: more information

Assignments will be formulated and presented in teams, and we foresee access to supercomputer platforms.

Evaluatieactiviteiten

Evaluation: Computational Methods for Astrophysical Applications (B-KUL-G2B30a)

Type : Continuous assessment without exam during the examination period
Description of evaluation : Report, Presentation, Participation during contact hours, Take-Home
Learning material : Course material, Computer

Explanation

Permanent assessment, working out project assignments. At least one project will be handed in as a written report, along with the self-written computercode. The team assignment lets the students perform modern computational research, to be reported in a team presentation.

Information about retaking exams

The second exam will be formulated as an extensive take-home computerassignment, where the student ultimately reports on the numerical strategy, (astro)physical application and makes contact with relevant modern literature.

ECTS Computergrafiek 2 (B-KUL-G0B36A)

4 studiepunten Nederlands 33 Tweede semesterTweede semester

Doelstellingen

Begrippen bijbrengen van enkele geavanceerde topics uit computer graphics, gesteund op recent wetenschappelijk onderzoek. De keuze van topics kan elk academiejaar aangepast worden. Op het einde van dit opleidingsonderdeel moeten studenten een voldoende kennis hebben over de werking van geavanceerde algoritmen in computer graphics, en moeten in staat zijn een kritische analyse te maken over het gebruik van deze algoritmen in een industriële context. Tevens moeten studenten vertrouwd zijn met de recente evoluties in de onderzoekswereld.

Begintermen

Er wordt verwacht dat studenten vertrouwd zijn met de volgende begrippen:
3D computer graphics, ray tracing, globale belichting, rendering vergelijking, materiaalmodellen voor computer graphics, texture-mapping, acceleratie-structuren voor computer graphics.
Typisch wordt deze inhoud behandeld in eerste computer graphics cursus. Aan KULeuven bestaat dergelijke inleidende cursus als: “Fundamenten van de Computergrafiek”.
Andere nuttige voorkennis (maar niet essentieel, vermits deze begrippen tevens behandeld worden in de cursus): numerieke integratie, kansrekenen, basisbegrippen uit radiometrie en fotometrie

Volgtijdelijkheidsvoorwaarden



GELIJKTIJDIG(G0Q66C) OF GELIJKTIJDIG(G0Q66D)


G0Q66CG0Q66C : Fundamenten van computergrafiek
G0Q66DG0Q66D : Fundamenten van computergrafiek

Onderwijsleeractiviteiten

Computergrafiek 2 (B-KUL-G0B36a)

2.5 studiepunten : College 20 Tweede semesterTweede semester

Inhoud

Een aantal thematische clusters, gebaseerd op recent onderzoek, komen aan bod. De keuze van de topics kan elk academiejaar aangepast worden.

Ter illustratie, de thema's aangeraakt gedurende 2021-2022:

  • Geavanceerde globale belichtingsalgoritmen
  • Shape en shape-analyse in computer graphics
  • Perceptie en waarneming in computer graphics
  • Alternatieve eveluaties van visibiliteitsberekeningen.

Studiemateriaal

Onderzoekspapers, ter beschikking gesteld via TOLEDO.

Toelichting werkvorm

  • 10 hoorcolleges
  • Enkele korte opdrachten, die telkens een aspect van de leerstof zullen behandelen. De nadruk ligt op het experimenteren met bestaande grafische programma’s, het uitwerken van wiskundige problemen, het kritisch evalueren van enkele papers.

Computergrafiek 2: Praktische opgaven (B-KUL-G0B37a)

1.5 studiepunten : Opdracht 13 Tweede semesterTweede semester

Inhoud

Enkele kortere opdrachten, die telkens een klein aspect van de leerstof zullen behandelen. De nadruk ligt het kritisch evalueren van papers, zelf nieuwe informatie opzoeken, een onderzoeksplan opstellen, e.d.

Studiemateriaal

Onderzoekspapers, ter beschikking gesteld via TOLEDO.

Evaluatieactiviteiten

Evaluatie: Computergrafiek 2 (B-KUL-G2B36a)

Type : Partiële of permanente evaluatie met examen tijdens de examenperiode
Evaluatievorm : Schriftelijk, Paper/Werkstuk

Toelichting

Timing praktische opdrachten: Gedurende het semester worden enkele opdrachten gegeven, waarvoor studenten telkens een twee- tot drietal weken tijd hebben om deze in te dienen. Nadien worden deze via individuele en/of groepsfeedback besproken.

De volledige evaluatie bestaat uit een schriftelijk examen en de praktische opdrachten gedurende het jaar. Bij een eventuele herkansing worden de opdrachten niet hernomen, maar worden de punten behaald op de opdrachten hernomen.

Het aandeel van de quotering van de opdrachten in het totaal kan 20% tot 30% uitmaken van het geheel (exact percentage kan variëren van academiejaar tot academiejaar, en afhankelijk van aantal en moeilijkheid praktische opdrachten).

Toelichting bij herkansen

De opdrachten worden niet hernomen gedurende een 2de examenkans, maar de punten van deze opdrachten blijven behouden.

ECTS Statistical Data Analysis (B-KUL-G0O00A)

6 ECTS English 26 Second termSecond term Cannot be taken as part of an examination contract

Aims

This course covers multivariate statistical methods for data analysis. The focus is on the practical use of these methods on real data, by means of the freeware statistical software R. The students will make a project where concrete data are given which are to be analysed by appropriate techniques, followed by interpretation and formulation of the results.

Upon completion of this course the student should

  • Know the main multivariate statistical techniques such as dimension reduction, clustering, regression, and classification;  
  • Know the strengths and weaknesses of these methods, and in which situations their use is appropriate;   
  • Have a critical attitude about each statistical method, know its underlying assumptions and how to verify them;
  • Be able to carry out these methods by means of the R software;
  • Be familiar with the resulting model diagnostics such as residuals and graphical displays;
  • Be able to interpret the results of the analysis and to report them in a scientific fashion.    

Previous knowledge

The students should have a good knowledge of basic mathematics as treated in “Lineaire algebra” and “Calculus I” in the bachelor of Mathematics (or similar courses). Moreover they should have followed at least one course in probability and statistics.

Is included in these courses of study

Onderwijsleeractiviteiten

Statistical Data Analysis (B-KUL-G0O00a)

3 ECTS : Lecture 12 Second termSecond term

Content

  • Multivariate data, covariance, checking normality assumption;
  • Transformation to normality by the Box-Cox transform;
  • Dimension reduction methods;
  • Cluster analysis: hierarchical and partitioning, graphical displays;
  • Topics in regression analysis: interactions, categorical predictors, heteroskedasticity, variable selection criteria, multicollinearity, ridge regression, outliers and leverage points, prediction models
  • Classification techniques: evaluation measures, misclassification rate, k-nearest neighbor classification, logistic regression, modern classification techniques

Course material

Course notes

Statistical Data Analysis: Exercises (B-KUL-G0O01a)

2 ECTS : Practical 12 Second termSecond term

Content

Weekly organised exercise sessions in the PC lab where the new methods (see OLA G0O00a) are illustrated and practised by means of the statistical software R. Some homework assignments need to be made as well.

Course material

Course notes and datasets

Statistical Data Analysis: Project (B-KUL-G0O02a)

1 ECTS : Assignment 2 Second termSecond term

Content

The projects consist of a thorough statistical analysis of real data. The results need to be presented in a written report.

Course material

Course notes and excercise material

Evaluatieactiviteiten

Evaluation: Statistical Data Analysis (B-KUL-G2O00a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Report, Skills test
Type of questions : Open questions

Explanation

The evaluation consists of two projects and an examination.  The projects involve data analysis tasks. For each project an individual written report is handed in with the analysis results presented in a scientific manner and an appendix describing the complete workflow. The written examination consists of a closed book part with open questions and an open book part on the computer which involves the analysis of a dataset.

The project part and exam part each count for 50% of the total course mark. Students should pass both parts to get a pass mark for the course.

Information about retaking exams

For the second chance exam, the project part and exam part again each count for 50% of the total course mark. This modality is the same for first and second exam chances.

Students that passed  the project work at the first exam chance can keep their score on this part for the second chance evaluation. Students that failed  the project work at the first exam chance will get a new project assignment for the second exam chance.

ECTS Number Theory (B-KUL-G0P61B)

6 ECTS English 46 Second termSecond term
N. |  Mohammadi Fatemeh (substitute)

Aims

Introducing the basic results and methods from elementary number theory. Applications and computational aspects are extensively discussed.

Previous knowledge

Courses G0N27A Lineaire Algebra, G0T45A Algebraïsche Structuren and G0N88A Algebra I.

Onderwijsleeractiviteiten

Number Theory (B-KUL-G0P61a)

4 ECTS : Lecture 26 Second termSecond term
N. |  Mohammadi Fatemeh (substitute)

Content

Review of basic arithmetics: Euler function, congruences of Euler and Wilson, Chinese Remainder Theorem.
Structure of the unit group of Zn.
Solubility of congruences: Lemma Hensel-Rychlik.
Quadratic reciprocity laws of Gauss and Jacobi.
Fast algorithms for congruences and primality testing.
The field of p-adic numbers.
p-adic numbers and the Hilbert symbol.
Rational points on a conic. The Hasse principle.
Quadratic rings.
Whole points on conic sections.
Applications in cryptography.
Prime numbers and the Riemann zeta function (introductory).
Elliptic curves

Course material

Syllabus

Format: more information

Lectures

Number Theory: Exercises (B-KUL-G0P62a)

2 ECTS : Practical 20 Second termSecond term
N. |  Mohammadi Fatemeh (substitute)

Content

Same as lectures.

Course material

Same as lectures + Toledo.

Format: more information

Exercises.

Evaluatieactiviteiten

Evaluation: Number Theory (B-KUL-G2P61b)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Take-Home

Explanation

The evaluation consists of:

- a written exam during the examination period (with grade E).
- 3 (non-obligatory) assignments during the semester (with grade T).

The final grade is calculated according to the formule max{E,(3E+T)/4}.

It is not possible to retake the assignments for the second examination attempt, but the previously submitted assignments do count for the final grade.

ECTS Fundamentals of Financial Mathematics (B-KUL-G0Q20A)

6 ECTS English 39 First termFirst term Cannot be taken as part of an examination contract

Aims

The aim of the course is to give a rigorous yet accessible introduction to the modern theory of financial mathematics.

Previous knowledge

- Sound mathematics, statistics and probability theory knowledge

Is included in these courses of study

Onderwijsleeractiviteiten

Fundamentals of Financial Mathematics (B-KUL-G0Q20a)

4 ECTS : Lecture 26 First termFirst term

Content

The aim of the course is to give a rigorous yet accessible introduction to the modern theory of financial mathematics. The student should already be comfortable with calculus and probability theory. Prior knowledge of basic notions of finance is useful.
We start with providing some background on the financial markets and the instruments traded. We will look at different kinds of derivative securities, the main group of underlying assets, the markets where derivative securities are traded and the financial agents involved in these activities. The fundamental problem in the mathematics of financial derivatives is that of pricing and hedging. The pricing is based on the no-arbitrage assumptions. We start by discussing option pricing in the simplest idealised case: the Single-Period Market. Next, we turn to Binomial tree models. Under these models we price European and American options and discuss pricing methods for the more involved exotic options. Monte-Carlo issues come into play here.
Next, we set up general discrete-time models and look in detail at the mathematical counterpart of the economic principle of no-arbitrage: the existence of equivalent martingale measures. We look when the models are complete, i.e. claims can be hedged perfectly. We discuss the Fundamental theorem of asset pricing in a discrete setting.
To conclude the course, we make a bridge to continuous-time models. We introduce and study the Black-Scholes model in detail.

Is also included in other courses

G0Q20C : Fundamentals of Financial Mathematics

Fundamentals of Financial Mathematics: Exercises (B-KUL-G0Q21a)

2 ECTS : Practical 13 First termFirst term

Evaluatieactiviteiten

Evaluation: Fundamentals of Financial Mathematics (B-KUL-G2Q20a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Paper/Project
Type of questions : Open questions
Learning material : Course material

Explanation

Features of the evaluation

* The evaluation consists of:

  • an assignment (25%)
  • an written exam (75 %)

* The deadline for the assignment will be determined by the lecturer and communicated via Toledo.

Determination of the final grade

* The grades are determined by the lecturer as communicated via Toledo and stated in the examination schedule. The result is calculated and communicated as a whole number on a scale of 20.

* The final grade is a weighted score and consists of:

  • the assignment: 25% of the final grade
  • the exam: 75% of the final grade

* If the student does not participate in the assignment and/or the exam, the grades for that part of the evaluation will be a 0-grade within the calculations of the final grade.

*If the set deadline for the assignment was not respected, the grade for that respective part will be a 0-grade in the final grade, unless the student asked the lecturer to arrange a new deadline. This request needs to be motivated by grave circumstances.

Second examination opportunity

* The features of the evaluation and determination of grades are similar to those of the first examination opportunity, as described above.

Information about retaking exams

* The features of the evaluation and determination of grades are similar to those of the first examination opportunity

ECTS Complexiteitstheorie (B-KUL-G0Q63B)

6 studiepunten Nederlands 40 Eerste semesterEerste semester Uitgesloten voor examencontract

Doelstellingen

Het doel van deze cursus is het bestuderen en begrijpen van een aantal complexiteitsklassen bij de theoretische analyse van computationale problemen. De student heeft inzicht in de onderlinge samenhang en robuustheid van de definities en het computationele model. De student heeft inzicht in klassen voor tijds- en ruimtecomplexiteit. De student begrijpt hoe de klassen P, NP en co-NP het begin zijn van de polynomiale-tijd-hiërarchie en het belang van de open vraag P ?= NP. De student begrijpt hoe het gebruik van willekeurige elementen resulteert in klassen zoals RP, co-RP, ZPP en BPP. De student begrijpt hoe de bewijzen werken en is in staat om zelf eenvoudige bewijzen te maken. De student is in staat om wetenschappelijke literatuur over complexiteitstheorie te begrijpen en toe te passen. 

Begintermen

De cursus steunt op kennis van programmeren, logica, statistiek en wiskundige bewijsvoering. Bij twijfel de docent contacteren. 

Onderwijsleeractiviteiten

Complexiteitstheorie: hoorcollege (B-KUL-G0Q63a)

4 studiepunten : College 20 Eerste semesterEerste semester

Inhoud

We behandelen de volgende onderwerpen (met mogelijke aanpassingen naar andere gerelateerde complexiteitsklassen): 

  • Het computationeel model (Turing machines) 
  • De basisklassen: P, NP, co-NP, NP-complete, EXP 
  • Diagonalisatie en zijn grenzen 
  • Ruimtecomplexiteit: PSPACE, NSPACE, L, NL 
  • De polynomiale hiërarchie en alternerende logische formules 
  • Booleaanse circuits, P/poly, NC 
  • Algoritmen met willekeurige elementen: BPP, RP, co-RP, ZPP 
  • Inleiding tot benaderende oplossingen 
  • Inleiding tot berekeningen met reële getallen en de relatie met de Turing machine 

Studiemateriaal

Diverse teksten aangeleverd door de docent en beschikbare standaardteksten (PDF). 

Toelichting werkvorm

De studenten bestuderen op voorhand het lesmateriaal in detail. In de les wordt een deel van het materiaal en de bewijzen interactief behandeld. De verdere bewijzen en voorbeeldalgoritmen worden door de student verwerkt in de oefenzitting. 

Complexiteitstheorie: oefeningen (B-KUL-H0O35a)

2 studiepunten : Practicum 20 Eerste semesterEerste semester

Inhoud

Bewijzen en oefeningen per topic. 

Studiemateriaal

Zelfde als hoorcollege. 

Toelichting werkvorm

Tijdens de oefenzitting werken de studenten verder door de bewijzen en voorbeeldalgoritmen. Zie ook de toelichting van de werkvorm bij het hoorcollege. 

Evaluatieactiviteiten

Evaluatie: Complexiteitstheorie (B-KUL-G2Q63b)

Type : Examen tijdens de examenperiode
Evaluatievorm : Schriftelijk
Vraagvormen : Open vragen, Gesloten vragen

Toelichting

Het examen is een gesloten boek schriftelijk examen dat nagaat of de student de bovenstaande doelstellingen behaald heeft. Het examen peilt naar inzicht, het begrijpen van de bewijzen en het kunnen gebruiken van deze bewijstechnieken. 

ECTS Fundamenten van computergrafiek (B-KUL-G0Q66D)

4 studiepunten Nederlands 30 Eerste semesterEerste semester Uitgesloten voor examencontract

Doelstellingen

Dit vak is een inleiding tot het ontwikkelen van grafische toepassingen in de computerwetenschappen. De nadruk ligt op de fundamentele algoritmen die aan de basis liggen van grafische toepassingen, zoals die gebruikt worden in allerhande modeleer- en visualizatietoepassingen.

In dit vak wordt voornamelijk ingegaan op de wiskundige theorie en het ontwikkelen van ray tracing als een algemene rendering-engine.

Begintermen

De volgende aspecten worden als voorkennis verondersteld voor dit vak: Programmeren; Algoritmen; Analytische meetkunde (vergelijkingen van rechten en vlakken in 3D), Lineaire algebra (vectoren en matrices, stelsels lineaire vergelijkingen). Kennis van toepassingen van meetkunde in de informatica is nuttig maar niet noodzakelijk. Kennis van integralen opstellen over een twee-dimensionaal integratiedomein is eveneens nuttig.

Onderwijsleeractiviteiten

Fundamenten van computergrafiek (B-KUL-H07Z6a)

3 studiepunten : College 20 Eerste semesterEerste semester

Inhoud

  • Inleiding tot ray tracing
  • Virtuele Camera & Perspectief
  • Transformaties en scène-grafe
  • Shading
  • Rendering vergelijking
  • Schaduwen en directe belichting
  • Acceleratiestructuren
  • Geavanceerde visuele effecten
  • Texture Mapping
  • Productie context

Studiemateriaal

Studiekost: 76-100 euro (De informatie over studiekosten zoals hier opgenomen is indicatief en geeft enkel de prijs weer bij aankoop van nieuw materiaal. Er zijn mogelijk ook e- en tweedehandskopijen beschikbaar. Op LIMO kan je nagaan of het handboek beschikbaar is in de bibliotheek. Eventuele printkosten en optioneel studiemateriaal zijn niet in deze prijs vervat.)

Handboek vanaf 2024:
Fundamentals of Computer Graphics, 5th edition, 2022
Steve Marschner, Peter Shirley
CRC Press/ Taylor and Francis
ISBN 9780367505035

Toelichting onderwijstaal

Hoorcolleges in het Nederlands, Examen in het Nederlands, studiemateriaal (boek, slides) in het Engels.

Fundamenten van computergrafiek: praktische opgaven (B-KUL-H0O59a)

1 studiepunten : Opdracht 10 Eerste semesterEerste semester

Inhoud

Enkele kortere opdrachten, die telkens een klein aspect van de leerstof zullen behandelen. De nadruk ligt op het kritisch evalueren van grafische software, zelf enkele experimenten uitvoeren, een klein algoritme zelf implementeren, e.d. De opdrachten worden beoordeeld met formatieve feedback.

Evaluatieactiviteiten

Evaluatie: Fundamenten van computergrafiek (B-KUL-G2Q66d)

Type : Examen tijdens de examenperiode
Evaluatievorm : Schriftelijk
Vraagvormen : Open vragen

Toelichting

Het opleidingsonderdeel wordt enkel geëvalueerd met examen gedurende de examenperiode. Gedurende het semester worden er wel enkel opdrachten opgegeven met formatieve feedback, maar die niet verrekend worden in de finale quotering voor dit vak.

Toelichting bij herkansen

Een herkansing gebeurt onder dezelfde modaliteiten als de eerste examenkans.

ECTS Data Visualization in Data Science (B-KUL-G0R72A)

4 ECTS English 20 Second termSecond term Cannot be taken as part of an examination contract

Aims

At the end of the course, students will:

  • have insight in the place of visualization in data analysis
  • have knowledge of the perception and cognition aspects of data visualization
  • be able to define and implement visualization approaches to support hypothesis and insight generation
  • have acquired the necessary skill set for design space exploration, both conceptually as by implementation

Identical courses

G0R72B: Data Visualization in Data Science

Is included in these courses of study

Onderwijsleeractiviteiten

Data Visualization in Data Science (B-KUL-G0R72a)

4 ECTS : Lecture 20 Second termSecond term

Content

As data becomes easier and cheaper to generate, we are moving from a hypothesis-driven to data-driven paradigm in scientific research. As a result, we don't only need to find ways to answer any questions we have, but also to identify interesting questions/hypotheses in that data in the first place. In other words: we need to be able to dig through these large and complex datasets in search for unexpected patterns that - once discovered - can be investigated further using regular statistics and machine learning. Interactive data visualization provides a methodology for just that: to allow the user (be they domain expert or lay user) to find those questions, and to give them deep insight in their data.

Content

  • Background and context of data visualization and visual data analysis
  • Design as a process: framing the problem, ideation, sketching, design critique, ...
  • Programming visualizations: static and dynamic
  • Project: visualization of expert dataset

Format: more information

The teaching methods used in this course aim to support the learning objectives as described above. In particular, general concepts will be explained in lectures and workshop-type sessions. In addition, students will apply these on small datasets during exercise sessions in order to get acquainted both with the design and the programming aspects. Finally, they will be asked to develop a data visualization project.

Is also included in other courses

G0R72B : Data Visualization in Data Science

Evaluatieactiviteiten

Evaluation: Data Visualization in Data Science (B-KUL-G2R72a)

Type : Continuous assessment without exam during the examination period
Description of evaluation : Project/Product

Explanation

The assessment will be a combination of permanent evaluation, designs created, and a project.

ECTS Engineering Economy (B-KUL-H00K1A)

3 ECTS English 31 First termFirst term

Aims

The student has a broad view on economical (decision oriented) problems engineers will encounter in their professional career.  The student obtains insights in methods such as PW, FW and AW (present worth, future worth and annual worth), C/B calculations (cost/benefit), replacement decision making and cost estimation.  The student assimilates these methods  to allow for real-life application.

Previous knowledge

bachelor engineering or other bachelor in science & technology

Is included in these courses of study

Onderwijsleeractiviteiten

Engineering Economy: Lecture (B-KUL-H00K1a)

2.44 ECTS : Lecture 20 First termFirst term

Content

The student has a broad view on economical (decision oriented) problems engineers will encounter in their professional career.  The student obtains insights in methods such as PW, FW and AW (present worth, future worth and annual worth), C/B calculations (cost/benefit), replacement decision making and cost estimation.  The student assimilates these methods  to allow for real-life application.

Course material

Study cost: 51-75 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

  • Blank, L. and Tarquin, A., Engineering Economy, 7th edition, McGraw-Hill, NY, 2012
  • academic papers

 

Language of instruction: more information

The course is taught and examined in English

Format: more information

ex cathedra + interactive discussions

Engineering Economy: Workshops (B-KUL-H03K2a)

0.56 ECTS : Practical 11 First termFirst term

Content

Exercises and cases (made available on Toledo beforehand) are solved and discussed. Case studies are exercises placed in a realistic business context, they call for more analysis and insight than classic exercises, as  data need to be distilled from a text describing a business situation, often there are too much data or not enough data (here assumptions are needed), .  Often they call for integration of concepts from different chapters. In the interpretation of the case study results the business context plays an important role: how important is the investment under study for the company in question?  how large can the negative investment value (present worth) be for a strategic investment?

Course material

exercises (Toledo)

Language of instruction: more information

English

Evaluatieactiviteiten

Evaluation: Engineering Economy (B-KUL-H20K1a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Multiple choice, Open questions
Learning material : List of formulas, Calculator

Explanation

As this exam is closed book, you cannot bring any written or printed material or laptop/tablet/ ... to the exam, only a simple calculator.  At the exam you will be provided with a formularium, it comes with the exam questions; you cannot bring your own.

ECTS Total Quality Management (B-KUL-H00N6A)

3 ECTS English 20 Second termSecond term

Aims

Introduction to Total Quality Management (TQM): philosophy and concepts (part I), tools and techniques (part II)

Identical courses

H00N6B: Total Quality Management

Is included in these courses of study

Onderwijsleeractiviteiten

Total Quality Management (B-KUL-H00N6a)

3 ECTS : Lecture 20 Second termSecond term

Content

The course Total Quality Management (TQM) begins with an introductory chapter which defines some basic concepts in TQM and also gives an overview of the historical evolution of TQM. The first part of the course discusses the TQM organisation for the industrial as well as for the service environment. In this part the role of management in TQM and the importance of the behaviour of workers are studied. Attention is given to the TQM implementation process and the development of a quality system, according ISO 9000. In the second part of the course quantitative techniques frequently used in TQM are studied, for example statistical process control, Pareto analysis, Ishikawa diagrams, etc...

Course material

Study cost: 76-100 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Goetsch, D.L. and Davis, S., Quality Management for Organizational Excellence: Introduction to Total Quality, 7th edition, Pearson, Boston, 2013

Language of instruction: more information

the course is taught and examined in English

Is also included in other courses

H00N6B : Total Quality Management

Evaluatieactiviteiten

Evaluation: Total Quality Management (B-KUL-H20N6a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Multiple choice, Open questions
Learning material : None

Explanation

A formularium will be provided.  For the multiple choice questions a guess correction  is used.

ECTS Computergesteund probleemoplossen (B-KUL-H01G1A)

3 studiepunten Nederlands 39 Eerste semesterEerste semester

Doelstellingen

Dit vak initieert de student in de methodologie voor het oplossen van technische problemen met behulp van de computer. Dit omvat enerzijds het aanleren van enkele belangrijke oplossingstechnieken zoals grafenalgoritmen en optimalisatiemethoden, en anderzijds het aanleren van de methodiek van het vertalen van een technisch probleem naar zo'n bestaande oplossingstechniek (het opstellen van een wiskundig model) en de interpretatie en analyse van de bekomen resultaten. Na deze cursus is de student in staat om:

  • een probleemstelling in doorlopende tekst om te zetten naar een optimalisatieprobleem, waarbij de beslissingsvariabelen, de beperkingen en de doelfunctie correct wiskundig geformuleerd worden;
  • het optimalisatiemodel te identificeren als een lineair model (LP), een geheeltallig model (IP), een niet-lineair model (NLP), een netwerk-model  of een model met gemengde karakteristieken;
  • de belangrijkste oplossingsmethoden en de daarbij horende  rekencomplexiteit voor deze verschillende modellen te situeren en in te schatten;
  • wiskundige optimalisatiemodellen te formuleren in de modelleringstaal LINGO, op te lossen met behulp van het LINGO-softwarepakket en de numerieke resultaten te interpreteren aan de hand van de sensitiviteitsrapporten;
  • enkele belangrijke netwerkalgoritmen (algoritme van Dijkstra, Kruskal, Prim, verhogend-pad) voor een eenvoudig grafen- en netwerkproblem met de hand uit te voeren.
     

Begintermen

Een basiskennis van analyse, algebra en numerieke wiskunde.

Onderwijsleeractiviteiten

Computergesteund probleemoplossen: hoorcollege (B-KUL-H01G1a)

2.2 studiepunten : College 16 Eerste semesterEerste semester

Inhoud

1: Inleiding: basisbegrippen voor het oplossen van problemen met de computer

  • classificatie en terminologie van optimalisatieproblemen
  • enkele typische voorbeeldproblemen

2: Lineaire programmering (LP) 

  • modelleren van optimalisatieproblemen als een LP-probleem
  • eigenschappen van een LP probleem en speciale gevallen
  • dualiteit in lineaire programmering en sensitiviteitsanalyse
  • oplossingsalgoritmen: simplexalgoritme, inwendige puntmethoden
  • enkele typische LP voorbeeldprobleemklassen

3: Grafenalgoritmen en netwerkmodellen 

  • basisdefinities van grafen
  • transport-, toewijzings- en verschepingsproblemen 
  • probleem van het kritieke pad – projectplanning 
  • probleem van het korste pad
  • probleem van de minimale opspannende boom
  • probleem van maximale stroming
  • technische toepassingen : routering, kritieke tijdsanalyse...

4: Geheeltallige programmering (IP) 

  • basisprincipes en eigenschappen van een IP-probleem
  • oplossingsmethodes: branch & bound, cutting planes
  • toepassingen : groeperings- en toewijzingsproblemen
  • voorbeeldproblemen van de handelsreiziger, van de knapzak en lineaire ordening

5: Gretige methodes en dynamische programmering

  • oplossen van problemen met heuristieken
  • gretige methodes
  • deterministische dynamische programmatie 
  • recursieprincipe
  • voorbeeldproblemen – meertrapsbeslissingsproblemen
  • toepassing : spraakherkenning

6: Speltheorie en beslissingsanalyse

  • coöperatieve en niet-coöperatieve spellen met 2 en meer spelers
  • oplossing als LP probleem
  • optimale strategie – Nash evenwicht
  • beslissingsbomen zonder en met onzekerheid

7: Niet-lineaire programmering (NLP) 

  • eigenschappen van NLP problemen
  • optimalisatie zonder beperkingen
  • optimalisatie met beperkingen
  • penaliserings- en barrièremethodes
  • nodige en voldoende voorwaarden : KKT voorwaarden 
  • optimalisatie met meerdere objectieven :
    - minimax – doelprogrammering
    - Pareto-optimaliteit en trade-off curven
  • quadratische en geometrische programmering 

8: Heuristische en stochastische methodes voor globale optimalisatie

  • gesimuleerd uitgloeien (simulated annealing)
  • genetische algoritmen
  • een situering van recente methodes : deeltjeszwermoptimalisatie, mierenkolonieoptimalisatie...

Studiemateriaal

Studiekost: 1-10 euro (De informatie over studiekosten zoals hier opgenomen is indicatief en geeft enkel de prijs weer bij aankoop van nieuw materiaal. Er zijn mogelijk ook e- en tweedehandskopijen beschikbaar. Op LIMO kan je nagaan of het handboek beschikbaar is in de bibliotheek. Eventuele printkosten en optioneel studiemateriaal zijn niet in deze prijs vervat.)

Handboek beschikbaar bij het VTK

Slides beschikbaar via Toledo

Toelichting werkvorm

Er zijn 10 hoorcolleges van 2 uur.

 

Computergesteund probleemoplossen: oefeningen (B-KUL-H01G2a)

0.8 studiepunten : Practicum 23 Eerste semesterEerste semester

Inhoud

Negen oefensessies bij de leerstof die in het hoorcollege aangebracht wordt. 

  • sessie 1-2:  LP problemen
  • sessie 3: LP problemen en gequoteerde oefening
  • sessie 4-5: Netwerkproblemen
  • sessie 6: Netwerkproblemen en gequoteerde oefening
  • sessie 7-8: IP problemen
  • sessie 9: speltheorie en gequoteerde oefening

 

 

Studiemateriaal

De opgaven bij de oefenzittingen worden via Toledo ter beschikking gesteld.

Toelichting werkvorm

9 oefenzittingen van 2.5uur onder begeleiding van een assistent. In drie van deze oefenzittingen wordt een gequoteerde opgave opgelost.

 

 

Evaluatieactiviteiten

Evaluatie: Computergesteund probleemoplossen (B-KUL-H21G1a)

Type : Partiële of permanente evaluatie met examen tijdens de examenperiode
Evaluatievorm : Mondeling, Schriftelijk
Vraagvormen : Open vragen
Leermateriaal : Rekenmachine

Toelichting

Het examen is gesloten boek en bestaat voornamelijk uit oefeningen. Alles wordt schriftelijk voorbereid. Twee vragen worden mondeling verdedigd; de andere drie worden schriftelijk ingediend. Dit examen telt mee voor 14 punten van de 20 in de eindquotering. Tijdens drie van de negen oefenzittingen krijgen de studenten een opgave die schriftelijk tijdens de zitting uitgewerkt en ingediend moet worden; de ingediende oplossingen worden beoordeeld en tellen in de eindquotering mee voor 6 punten van de 20.

Wanneer de faculteit wegens overmacht beslist dat een schriftelijk examen en een mondelinge toelichting niet combineerbaar zijn, dan vervalt de mondelinge toelichting. Het resultaat van het examen wordt
dan volledig op basis van de schriftelijke antwoorden bepaald.

Wanneer de faculteit wegens overmacht beslist dat de oefenzittingen in hun huidige vorm niet kunnen doorgaan, tellen enkel de reeds afgelegde oefenzittingen mee voor de eindquotering, a rato van 2 punten per gekwoteerde oefenzitting, waarbij het examen dan relatief meer gewicht krijgt (nl. voor 16, 18 of 20 punten).

ECTS Toegepaste discrete algebra (B-KUL-H01G5A)

3 studiepunten Nederlands 30 Tweede semesterTweede semester
Preneel Bart (coördinator) |  Preneel Bart |  Rijmen Vincent

Doelstellingen

Het doel van deze cursus is inzicht te verwerven in (eindige) algebraïsche structuren en deze te kunnen herkennen en de eigenschappen ervan te kunnen gebruiken zoals ze voorkomen in ingenieurstoepassingen in het domein van de informatie- en communicatietechnologie  (gegevensstructuren, cryptografie, codetheorie, wiskundige modellen, ...). De nadruk ligt op het vlot kunnen werken met deze structuren en de eigenschappen ervan eerder dan op de theoretische studie en het rigoureus bewijzen van alle eigenschappen. Het is de bedoeling dat de student leert deze begrippen en methodieken gebruiken in oefeningen en opdrachten.. Om dit te illustreren worden een aantal dergelijke toepassingen reeds aangebracht.

Begintermen

Wiskunde uit het middelbaar onderwijs met minstens 6 lestijden wiskunde.

Volgtijdelijkheidsvoorwaarden



GELIJKTIJDIG (H01A4B) OF GELIJKTIJDIG (X0A02C)


H01A4BH01A4B : Toegepaste algebra
X0A02CX0A02C : Lineaire algebra


Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Toegepaste discrete algebra: hoorcollege (B-KUL-H01G5a)

2.7 studiepunten : College 20 Tweede semesterTweede semester

Inhoud

1. Verzamelingen, relaties, functies 
algebra van verzamelingen, productverzameling 
relaties (m.i.v. equivalentierelaties en orderelaties) 
functies (injectie, surjectie, bijectie, samenstelling van functies), afbeeldingen 
recursie, inductie 
kardinaalgetallen, aftelbaarheid 


2. Logica en Booleaanse algebra 
propositielogica, waarheidstabellen, normaalvormen 
kwantoren, predicatenlogica 
Booleaanse algebra 
toepassing:logische schakelingen 
isomorfisme (verband tussen verzamelingen, logica en Booleaanse algebra) 


3. Algebraïsche structuren 
groepen 
- definitie, Abelse groep, cyclische groep, permutatiegroep 
- orde van een element, exponent van de groep 
- deelgroepen (normaaldeler), quotiëntstructuur, stelling van Lagrange
- classificatie van eindige groepen
- getallenleer
ringen 
- definitie, ring met eenheid, nuldelers, vereenvoudigingswet, modulorekenen 
- Euclidische domeinen, algoritme van Euclides, stelling van Bezout-Bachet 
- idealen, quotiëntstructuur, priemideaal, maximaal ideaal, principaal ideaal 
velden 
- definitie, eindige velden, karakteristiek van een veld 
- veeltermen over ringen en velden 
- veeltermideaal, quotiëntstructuur, uitbreidingsvelden 
- Galoisvelden 
- vectorruimten over eindige velden 
toepassingen 
- foutverbeterende code van de CD speler 
- publieke sleutel cryptografie

Studiemateriaal

Studiekost: 1-10 euro (De informatie over studiekosten zoals hier opgenomen is indicatief en geeft enkel de prijs weer bij aankoop van nieuw materiaal. Er zijn mogelijk ook e- en tweedehandskopijen beschikbaar. Op LIMO kan je nagaan of het handboek beschikbaar is in de bibliotheek. Eventuele printkosten en optioneel studiemateriaal zijn niet in deze prijs vervat.)

Cursustekst met oefeningen. 
Deel II van de cursustekst bevat opgeloste oefeningen.

Toelichting werkvorm

De les begint met 1 uur theorie.  Vervolgens worden een 3-tal oefeningen besproken die op voorhand werden opgelost door een groep studenten. Hierbij wordt feedback gegeven en wordt de theorie verduidelijkt. De les eindigt met een kort overzicht van de volgende les.

Toegepaste discrete algebra: oefeningen (B-KUL-H01G6a)

0.3 studiepunten : College 10 Tweede semesterTweede semester

Inhoud

Oefeningen over leerstof.

Studiemateriaal

Cursustekst.

Toelichting werkvorm

Oplossen van oefeningen. Er wordt verwacht dat de studenten deze oefeningen voorbereiden.

Evaluatieactiviteiten

Evaluatie: Toegepaste discrete algebra (B-KUL-H21G5a)

Type : Examen tijdens de examenperiode
Evaluatievorm : Schriftelijk
Vraagvormen : Gesloten vragen, Open vragen
Leermateriaal : Cursusmateriaal

Toelichting

Het examen bestaat uit vier vragen die elk voor 25% verrekend worden in het eindresultaat. De eerste drie vragen zijn oefeningen. Een vierde vraag geeft een 5-tal beweringen waarvoor moet aangegeven worden of ze waar of fout zijn; als de bewering waar is, wordt gevraagd om ze te bewijzen; als ze fout is, moet een tegenvoorbeeld gegeven worden en moet de bewering verbeterd worden.

Het examen is open boek. Deel I van de cursustekst mag gebruikt worden, maar er mogen geen opgeloste oefeningen worden bijgeschrijven. Deel II van de cursustekst met opgeloste oefeningen mag niet gebruikt worden.

 

ECTS Digitale signaalverwerking (B-KUL-H01L6A)

3 studiepunten Nederlands 28 Eerste semesterEerste semester

Doelstellingen

Na het succesvol volgen van dit OPO heeft de student deze vaardigheden verworven:

  • De student kent de algemene architectuur van digitale systemen voor signaalverwerking en kan de belangrijkste voor- en nadelen ten opzichte van analoge signaalverwerking bespreken.
  • De student kan continue en discrete tijdssignalen en beelden beschrijven in het frequentiedomein aan de hand van de continue en discrete Fourier transformatie, en kan de effecten van bemonstering en interpolatie op deze frequentiebeschrijving analyseren binnen dit theoretisch kader.
  • De student kan het gedrag van lineaire filters analyseren in het frequentiedomein, en kan eenvoudige digitale filters en hun realisatiestructuur ontwerpen, ook in multirate systemen.
  • De student kan veelvoorkomende toepassingen uit het audiovisuele domein situeren binnen het theoretische kader voor signaalverwerking.

Begintermen

Basisbegrippen van lineaire systeemtheorie en van lineaire differentiaalvergelijkingen; basiskennis van transformaties zoals de Laplace- en Z-transformatie, en van Fourierreeksen.

Volgtijdelijkheidsvoorwaarden



((GELIJKTIJDIG (H01D2A) OF GELIJKTIJDIG(H01D2D) OF GELIJKTIJDIG (H01D2C)) OF GELIJKTIJDIG (X0C76A) OF GELIJKTIJDIG (X0C76B) OF GELIJKTIJDIG( X0E60A ) OF GELIJKTIJDIG(X0F53A) OF GELIJKTIJDIG (X0B89B)) EN (GELIJKTIJDIG (H01M8A) OF GELIJKTIJDIG (X0B91A) OF GELIJKTIJDIG(H0R57A) OF GELIJKTIJDIG (H08U4A))


H01D2AH01D2A : Informatieoverdracht en -verwerking
H01D2DH01D2D : Informatieoverdracht en -verwerking
H01D2CH01D2C : Informatieoverdracht en -verwerking
X0C76AX0C76A : Informatieoverdracht en -verwerking m.i.v. elektrische netwerken
X0C76BX0C76B : Informatieoverdracht en -verwerking m.i.v. elektrische netwerken
X0E60AX0E60A : Informatieoverdracht en –verwerking
X0F53AX0F53A : Informatieoverdracht en -verwerking
X0B89BX0B89B : Informatieoverdracht en -verwerking
H01M8AH01M8A : Systeemtheorie en regeltechniek
X0B91AX0B91A : Systeemtheorie en regeltechniek
H0R57AH0R57A : Systeemtheorie en regeltechniek
H08U4AH08U4A : Systeemtheorie


Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Digitale signaalverwerking: hoorcollege (B-KUL-H01L6a)

2.6 studiepunten : College 18 Eerste semesterEerste semester

Inhoud

Dit is een overzicht van de materie die aan bod komt:

1 Inleiding
   1.1 Digitale signalen en systemen
   1.2 Signalen als functies van de tijd
   1.3 Systemen en ingans/uitgangsrelaties

2 Signaaltransformaties en frequentiebeschrijving
   2.1 Signaal- en systeemanalyse met eigenfuncties
   2.2 Basistheorie van de analytische signaaltransformaties
   2.3 Basistoepassingen van de analytische transformaties

3 Bemonstering en kwantisatie
   3.1 Bemonstering
   3.2 Bijzondere bemonsteringstechnieken
   3.3 Kwantisatie

4 De discrete Fourier transformatie
   4.1 Afleiding en definitie
   4.2 Eigenschappen
   4.3 Rand-effecten en vensterfuncties
   4.4 Het FFT-algoritme
   4.5 Meervoudige banddoorlaatfiltering
   4.6 Globaal overzicht van de signaaltransformaties

5 Gerelateerde signaaltransformaties
   5.1 De short-time Fourier transformatie
   5.2 De Discrete Cosinus Transformatie

6 Analyse en realisatie van digitale filters
  5.1 Beschrijving en analyse van filters
  5.2 Realisatiestructuren voor digitale filters

7 Meerdimensionale signaalanalyse
  6.1 Tweedimensionale Fourier transformaties
  6.2 Tweedimensionale DFT
  6.3 Tweedimensionale convolutie en correlatie
  6.4 Tweedimensionale eindige convolutie en correlatie
  6.5 Andere tweedimensionale transformaties
  6.6 Toepassing: beeldverwerking

8 Multirate signaalverwerking
  8.1 Decimatie en interpolatie
  8.2 Transformatie van gedecimeerde en ge¨ýnterpoleerde sequenties
  8.3 Lineaire filtering bij multirate systemen
  8.4 Verandering van bemonsteringsfrequentie
  8.5 Structuren voor multirate systemen
  8.6 De polyfase voorstelling
  8.7 Implementatie van multirate systemen
 

Studiemateriaal

Studiekost: 1-10 euro (De informatie over studiekosten zoals hier opgenomen is indicatief en geeft enkel de prijs weer bij aankoop van nieuw materiaal. Er zijn mogelijk ook e- en tweedehandskopijen beschikbaar. Op LIMO kan je nagaan of het handboek beschikbaar is in de bibliotheek. Eventuele printkosten en optioneel studiemateriaal zijn niet in deze prijs vervat.)

Eigen nederlandstalige cursustekst, verkrijgbaar bij VTK.

Toelichting werkvorm

Negen klassieke lessen

Digitale signaalverwerking: oefeningen (B-KUL-H01L7a)

0.4 studiepunten : Practicum 10 Eerste semesterEerste semester

Inhoud

Er zijn vier oefenzittingen die de belangrijkste delen van de cursus bestrijken :

  • de Fouriertransformatie (analoog, digitaal, eigenschappen, …)
  • bijzondere technieken: bemonstering van banddoorlaatsignalen, quadratuurbemonstering
  • analyse van digitale filters, digitale filterstructuren
  • multirate signaalverwerking 

Oefeningen over andere onderwerpen uit de cursus worden ter zelfstudie aangeboden op Toledo (met enkele oplossingen).

Studiemateriaal

Opgaven die ook op Toledo te vinden zijn. Enige tijd na de desbetreffende oefenzitting, zijn ook een deel van de opgeloste oefeningen op Toledo te vinden.

Toelichting werkvorm

Deze OLA bestaat uit vier klassieke oefenzittingen in een klaslokaal, begeleid door een assistent(e).

Evaluatieactiviteiten

Evaluatie: Digitale signaalverwerking (B-KUL-H21L6a)

Type : Examen tijdens de examenperiode
Evaluatievorm : Schriftelijk
Vraagvormen : Open vragen
Leermateriaal : Formularium, Rekenmachine

Toelichting

Een formularium is ter beschikking en mag gebruikt worden op het examen. Het examen bestaat zowel uit theorievragen als uit oefeningen.

ECTS Systeemtheorie en regeltechniek (B-KUL-H01M8A)

6 studiepunten Nederlands 56 Tweede semesterTweede semester

Doelstellingen

  • Bestuderen van de belangrijkste (analyse)methodes uit de systeemtheorie en regeltechniek. Dit vormt de basis voor vervolgcursussen over het ontwerp van systemen in de elektrotechniek, automatisering, digitale signaalverwerking en informatieoverdracht.
  • Inoefenen van de methodes bestudeerd in de hoorcolleges, door middel van het oplossen van oefeningen en ex-examenvragen.
  • Aanleren van relevante technieken in MATLAB.

Begintermen

Lineaire algebra, lineaire differentiaalvergelijkingen, toegepaste algebra, fundamenten van de informatieoverdracht en -verwerking.

Volgtijdelijkheidsvoorwaarden



GELIJKTIJDIG(H01D2A ) OF GELIJKTIJDIG(H01D2C ) OF GELIJKTIJDIG(H01D2D) OF GELIJKTIJDIG(X0C76A) OF GELIJKTIJDIG(X0C76B) OF GELIJKTIJDIG(X0E60A) OF GELIJKTIJDIG(X0F53A) OF GELIJKTIJDIG(X0B89B)


H01D2AH01D2A : Informatieoverdracht en -verwerking
H01D2CH01D2C : Informatieoverdracht en -verwerking
H01D2DH01D2D : Informatieoverdracht en -verwerking
X0C76AX0C76A : Informatieoverdracht en -verwerking m.i.v. elektrische netwerken
X0C76BX0C76B : Informatieoverdracht en -verwerking m.i.v. elektrische netwerken
X0E60AX0E60A : Informatieoverdracht en –verwerking
X0F53AX0F53A : Informatieoverdracht en -verwerking
X0B89BX0B89B : Informatieoverdracht en -verwerking


Identieke opleidingsonderdelen

X0B91A: Systeemtheorie en regeltechniek

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Systeemtheorie en regeltechniek: hoorcollege (B-KUL-H01M8a)

5.2 studiepunten : College 36 Tweede semesterTweede semester

Inhoud

  • Discrete- en continuetijdssytemen in het tijds- en frequentiedomein
  • Discretisatie, kwantisatie en reconstructie van signalen en systemen
  • Ontwerptechnieken (e.g., wortellijnen, Bodediagrammen en Nyquistdiagrammen)
  • Regeltechnieken (e.g., proportionele regelaars, lag-lead-regelaars en PID-regelaars)

Studiemateriaal

Studiekost: 1-10 euro (De informatie over studiekosten zoals hier opgenomen is indicatief en geeft enkel de prijs weer bij aankoop van nieuw materiaal. Er zijn mogelijk ook e- en tweedehandskopijen beschikbaar. Op LIMO kan je nagaan of het handboek beschikbaar is in de bibliotheek. Eventuele printkosten en optioneel studiemateriaal zijn niet in deze prijs vervat.)

Een digitale cursus wordt online beschikbaar gesteld op Toledo.

Toelichting werkvorm

Er worden twee lessen per week ingepland.  Elk lesmoment duurt twee uur.

Systeemtheorie en regeltechniek: oefeningen (B-KUL-H01M9a)

0.8 studiepunten : Practicum 20 Tweede semesterTweede semester

Inhoud

 Acht oefenzittingen, waarvan twee computersessies.

Studiemateriaal

Het materiaal voor de oefenzittingen wordt online beschikbaar gesteld op Toledo.

Toelichting werkvorm

Begeleide oefenzittingen, waarbij de studenten de kans krijgen om vragen te stellen over de oefeningen en de bijhorende theorie.

Evaluatieactiviteiten

Evaluatie: Systeemtheorie en regeltechniek (B-KUL-H21M8a)

Type : Examen tijdens de examenperiode
Evaluatievorm : Schriftelijk
Vraagvormen : Open vragen
Leermateriaal : Cursusmateriaal, Rekenmachine

Toelichting

Schriftelijk openboekexamen met een eenvoudige niet-grafische rekenmachine.

ECTS Objectgericht programmeren (B-KUL-H01P1A)

6 studiepunten Nederlands 36 Tweede semesterTweede semester
Jacobs Bart (coördinator) |  Devriese Dominique |  Jacobs Bart

Doelstellingen

De focus van dit OPO ligt op de belangrijkste methode voor het beheersen van de complexiteit van de ontwikkeling, het onderhoud, en de evolutie van middelgrote en grote softwaresystemen: modulair programmeren. In het modulair programmeren wordt het softwaresysteem opgedeeld in modules, elk voorzien van een duidelijke en abstracte specificatie die de syntax en het gedrag definieert van de programmeerinterface of API die de module aanbiedt aan haar klant-modules, zodanig dat de correctheid van een klant-module nagegaan kan worden louter op basis van de specificatie, niet de implementatie, van de modules die ze gebruikt. Hierdoor kan elke module onafhankelijk van en gelijktijdig met de andere modules ontwikkeld, begrepen, geverifieerd en bijgewerkt worden. De belangrijkste modularisatie-aanpak is abstractie, waarbij de module de programmeertaal uitbreidt met extra bewerkingen (procedurale abstractie) of datatypes (data-abstractie).

De beginselen van het modulair programmeren worden aangebracht in de context van de objectgerichte programmeertaal Java.

Op het einde van de cursus moeten studenten in staat zijn om:

  • Een middelgroot softwaresysteem te ontwerpen als een compositie van procedurale abstracties en data-abstracties, desgevallend gebruikmakend van overervingshiërarchieën.
  • De nodige documentatie te verzorgen voor de modules van een middelgroot softwaresysteem. Concreet leren studenten hoe zowel informele, natuurlijke talen, als meer formele notaties kunnen gebruikt worden in de documentatie van klassen en methodes.
  • De implementatie uit te werken voor een middelgroot softwaresysteem in Java.
  • De correctheid van een middelgroot softwaresysteem te verifiëren. Op de eerste plaats leren studenten hoe op een systematische manier kan geredeneerd worden over de correctheid van diverse stukken code. Verder krijgen ze inzicht in strategieën om de correcte werking van een softwaresysteem op een systematische manier te testen.

Aan de cursus is een project gekoppeld waar studenten leren de principes van het modulair objectgericht programmeren correct en rigoreus toe te passen. Daarnaast leren ze om te gaan met een stijgende complexiteit in de ontwikkeling van softwaresystemen, en met niet volledig exact gespecificeerde beschrijvingen.

Begintermen

  • Basisbeginselen van imperatief programmeren met inzicht in het iteratief en recursief ontwikkelen van algoritmes.
  • Basisbeginselen van eerste-orde logica.

Identieke opleidingsonderdelen

X0A27A: Objectgericht programmeren

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Objectgericht programmeren: hoorcollege (B-KUL-H01P1a)

4 studiepunten : College 32 Tweede semesterTweede semester

Inhoud

De cursus behandelt uitsluitend het objectgerichte paradigma voor het ontwikkelen van softwaresystemen. Daartoe wordt gebruik gemaakt van Java als programmeertaal, gekoppeld aan een kleine subset van de modelleertaal UML voor het visualiseren van het ontwerp. In de documentatie wordt verder gebruik gemaakt van een beperkte vorm van eerst-orde logica om bepaalde aspecten op een meer formele manier te kunnen uitdrukken.

De inhoud van de cursus omvat vier grote delen. Een eerste deel behandelt de definitie van procedurale abstracties alsook de eenvoudigste soort data-abstracties, nl. één-object-abstracties, die gerealiseerd worden door één klasse waarvan elk object overeenkomt met een aparte instantie van de abstractie. Daarbij wordt de nodige aandacht besteed aan het behandelen van randgevallen in de definitie van methodes. In een tweede deel wordt ingegaan op de ontwikkeling van hiërarchieën van klassen. Het principe van behavioral subtyping staat daarin centraal. Het derde deel gaat over graafabstracties, waarbij een graaf van entiteiten uit het probleemdomein met hun relaties voorgesteld wordt door een verzameling van objecten verbonden door middel van bidirectionele associaties. Een laatste deel behandelt de ontwikkeling van geneste klassen en van generische klassen.
 
1. Eén-object-abstracties

Een essentieel element in de ontwikkeling van grote en middelgrote softwaresystemen is de opdeling in een aantal modules. Klassen spelen daarin een essentiële rol. Ze introduceren een verzameling van objecten samen met een geheel van bewerkingen die op die objecten toepasbaar zijn. In dit deel van de cursus wordt de notie van een klasse uitgediept en worden de concepten bestudeerd hoe ze op het niveau van objectgerichte programmeertalen kunnen worden ontwikkeld. Belangrijke aspecten in deze studie zijn:

  • De specificatie van een klasse: het vastleggen van de signatuur van methodes en een omschrijving van de semantiek in termen van precondities, postcondities, uitzonderingen en invarianten.
  • De implementatie van een klasse: het bepalen van een geschikte interne representatie en het realiseren van het effect van de gespecificeerde methodes.
  • De verificatie van een klasse aan de hand van een studie van diverse strategieën voor het systematisch testen van softwaresystemen.
  • Het omgaan met uitzonderlijke omstandigheden via de paradigma's van het defensief programmeren en het contractueel programmeren.
  • De encapsulatie van de representatie van een klasse, en de gevaren van de blootstelling van representatie-objecten aan de klant.

2. Overerving

Objectgerichte programmeertalen ondersteunen de definitie van nieuwe klassen als speciale gevallen van bestaande klassen. Dit geeft aanleiding tot een hiërarchische structuur, die ook tijdens de uitvoering van het programma kan worden uitgebuit. Belangrijke aspecten in dit deel zijn:

  • Abstracte superklassen ter veralgemening van een stel concrete klassen.
  • Polymorfisme en dynamische binding als mechanismen om klantcode te ontkoppelen van de concrete klasse van een object.
  • Gedragssubtypering (behavioral subtyping) als een algemene leidraad in de ontwikkeling van hiërarchiëen.
  • Implementatie-overerving om een klasse te definiëren als een uitbreiding of aanpassing van een bestaande klasse.
  • Meervoudige overerving als middel om een nieuwe klasse af te leiden van één bestaande klasse en een onbeperkt aantal interfaces.

3. Graafabstracties

Zeer dikwijls moet een computerprogramma gegevens verwerken in de vorm van een graaf: een stel entiteiten voorzien van relaties en attributen. In een objectgericht programma wordt een graaf typisch voorgesteld door middel van een stel objecten verbonden door bidirectionele associaties. In dit deel wordt ingegaan op de realisatie van dergelijke graafabstracties. Belangrijke onderwerpen in dit verband zijn:

  • Eénklassige graafabstracties, waarbij alle entiteiten van eenzelfde type zijn.
  • Meerklassige graafabstracties, en het encapsuleren van meerklassige modules door middel van packages en default accessibility.
  • Genestelde abstracties: het opbouwen van abstracties geëncapsuleerd op package-niveau uit abstracties geëncapsuleerd op klasseniveau.

4. Geavanceerde taalconstructies

In moderne programmeertalen kunnen klassen gedefinieerd worden binnen het lichaam van andere klassen, wat resulteert in de notie van geneste klassen. Sinds versie 8 biedt Java ondesteuning voor aspecten van het functioneel programmeren via streams en lambda-uitdrukkingen. Klassen kunnen ook geparameteriseerd worden, wat resulteert in de notie van generische klassen. Meestal beperken argumenten van generische klassen zich tot types. In dit deel worden de typische elementen bestudeerd die aan bod komen in de ontwikkeling van geneste klassen en van generische klassen.

  • Studie van iteratoren als generische instrumenten om gegevensstructuren op allerlei manieren te doorlopen.
  • Studie van geneste klassen, met de nadruk op de definitie van anonieme klassen.
  • Studie van lambda-uitdrukkingen als een middel om functies door te geven aan methodes.
  • Definitie en instantiatie van generische klassen.
  • Definitie van beperkingen op parameters van generische klassen.

Studiemateriaal

  • Handboek: Prof. Jacobs bereidt een nieuwe cursustekst voor, die vanaf academiejaar 2019-2020 gebruikt zal worden.
  • Toledo: modeloplossingen van oefeningen, projectopgave, verwijzingen naar softwarewerktuigen en aanvullende literatuur op Toledo.

Toelichting werkvorm

In een 13-tal sessies wordt de behandelde materie doorgenomen aan de hand van een typisch probleem. De oplossing voor het gestelde probleem wordt interactief uitgewerkt in het auditorium. Van de studenten wordt verwacht dat ze vooraf de behandelde materie hebben doorgenomen in de cursustekst. Ieder probleem wordt op het einde van de sessie uitgebreid met een vrijblijvende huistaak. Van ieder probleem (inclusief de huistaak) wordt achteraf een modeloplossing ter beschikking gesteld.

Objectgericht programmeren: praktijk (B-KUL-H01P2a)

2 studiepunten : Opdracht 4 Tweede semesterTweede semester

Toelichting werkvorm

De cursus wordt aangevuld met een project, waarvan de opgave stapsgewijze verspreid wordt. Het project wordt bij voorkeur uitgewerkt in groepjes van 2 studenten, volgens de principes van Extreme Programming. Gedurende het ganse project kunnen studenten voor een vooraf opgegeven aantal uren terecht bij een team van begeleiders. Het project bepaalt, samen met het schriftelijk examen, het resultaat voor het OPO.

  • Deel 1: ontwikkeling van één-object-abstracties.
  • Deel 2: ontwikkeling van klassenhiërarchieën.
  • Deel 3: ontwikkeling van graafabstracties.

De deadlines en het relatieve gewicht van de drie delen wordt bekendgemaakt via Toledo.

Evaluatieactiviteiten

Evaluatie: Objectgericht programmeren (B-KUL-H21P1a)

Type : Partiële of permanente evaluatie met examen tijdens de examenperiode
Evaluatievorm : Schriftelijk, Ontwerp/Product
Vraagvormen : Open vragen
Leermateriaal : Geen

Toelichting

De evaluatie bestaat uit twee luiken: een project tijdens het semester, en een individueel schriftelijk examen op PC tijdens de examenperiode. Om te slagen voor het OPO moet de student slagen voor het project én voor het schriftelijk examen.

De opgave voor het project wordt in de loop van het semester in 3 opeenvolgende stappen opgegeven. Elke volgende opgave wordt vrijgegeven na de deadline voor indiening van de oplossing van de vorige opgave. De finale oplossing moet ingeleverd worden tegen het einde van het semester.

  • Precieze informatie over de timing verschijnt op Toledo en in de opgave van de diverse delen.
  • Het project wordt bij voorkeur uitgewerkt in groepjes van 2 studenten. Studenten die ervoor kiezen het project alleen te maken moeten rekening houden met een verhoogde werklast: de opgave wordt voor hen slechts licht of helemaal niet gereduceerd.
  • Het resultaat voor het project is het gewogen gemiddelde van een resultaat per deel, met gewichten die worden bekendgemaakt via Toledo.

Het schriftelijk examen is gesloten boek en toetst de kennis van, het inzicht in, en de vaardigheid bij het toepassen van de begrippen en principes van het OPO, aan de hand van verschillende soorten vragen, waaronder bijvoorbeeld:

  • invulvragen waarbij de theorie- en terminologiekennis getoetst wordt;
  • programmeertaken waarbij de student een handvol bladzijden Java-code en documentatie moet intikken op een PC.

Het resultaat voor het OPO (vóór afronding) is het gemiddelde van het resultaat voor het project en het resultaat voor het schriftelijk examen, behalve als het resultaat voor het project of het resultaat voor het schriftelijk examen (of beide) kleiner is dan 10/20. In dat geval is het resultaat voor het OPO het minimum van het resultaat voor het project en het resultaat voor het schriftelijk examen. (Merk op: enkel het resultaat voor het OPO als geheel wordt afgerond; deelcijfers worden niet afgerond.)

Toelichting bij herkansen

Wie niet geslaagd is voor het OPO in juni, kan deelnemen aan de herkansing van het project, aan de herkansing van het schriftelijk examen, of aan beide, naar keuze.

Merk op: studenten moeten zich inschrijven voor de herkansing voor dit OPO, zelfs als ze enkel deelnemen aan de herkansing van het project.

Voor de septemberzittijd wordt een nieuwe projectopgave ("deel 4") verspreid. De deadline wordt bekendgemaakt via Toledo.

Studenten kunnen voor deze zittijd een nieuw team vormen. Ze kunnen ook opteren om het project alleen af te werken.

Het resultaat voor de herkansing van het project vervangt (als de student een oplossing indient) het resultaat voor het project in juni, ook als het slechter is.

Het resultaat voor de herkansing van het schriftelijk examen vervangt het resultaat voor het schriftelijk examen in juni, tenzij het slechter is.

Het resultaat voor het OPO wordt op dezelfde wijze berekend als in juni.

ECTS Numerieke benadering met toepassing in datawetenschappen (B-KUL-H01P3A)

6 studiepunten Nederlands 54 Tweede semesterTweede semester Uitgesloten voor examencontract
Michiels Wim (coördinator) |  Michiels Wim |  Samaey Giovanni

Doelstellingen

De benadering van functionele verbanden tussen grootheden en de interpretatie van data zijn universele problemen in de (ingenieurs-)wetenschappen met vele toepassingen, onder meer in de datawetenschappen en in machine learning. Deze cursus behandelt een aantal belangrijke numerieke methoden en algoritmen voor het benaderen van een gekende functie door een combinatie van eenvoudigere functies, het bepalen van een ongekende functie op basis van mogelijk grote hoeveelheden (gemeten) data, en voor de analyse van datasets en grafen. Daarbij wordt aandacht besteed aan de kwaliteit van de bekomen oplossingen, de rekencomplexiteit en numerieke eigenschappen van de algoritmes om die oplossingen te berekenen, en de brede  toepasbaarheid van de aangereikte theorie en algoritmes. In de cursus komen zowel eendimensionale als meerdimensionale benaderingsproblemen aan bod. 

 

Na deze cursus zal de student in staat zijn om: 

  • standaard benaderingstechnieken te beschrijven en hun eigenschappen (complexiteit, nauwkeurigheid, betrouwbaarheid) kritisch te bespreken; 
  • een gefundeerde keuze te maken voor specifieke benaderingstechnieken, afhankelijk van de context en de probleemstelling; 
  • benaderingsalgoritmes te implementeren en de bekomen numerieke resultaten te interpreteren in functie van de eigenschappen van de methodes; 
  • specifieke problemen in datawetenschappen te formuleren als een benaderingsprobleem, numeriek op te lossen en het oplossingsproces helder schriftelijk te rapporteren. 

Begintermen

Deze cursus steunt op cursussen analyse, lineaire algebra en numerieke wiskunde zoals die bijvoorbeeld aangeboden worden in de eerste 3 semesters van bachelor ingenieurswetenschappen, en veronderstelt een vertrouwdheid met 
toepassingsdomeinen zoals systeemtheorie, informatie-overdracht, mechanica/natuurkunde. 

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Numerieke benadering met toepassing in datawetenschappen: hoorcollege (B-KUL-H01P3a)

4 studiepunten : College 34 Tweede semesterTweede semester

Inhoud

Deel 1 Inleiding   

  • Data en model: Wat is een benaderingsprobleem; Van data naar functiebenadering 
  • Beste benadering: Definitie van een optimalisatieprobleem; Regularisatie; Lineaire vs. niet-lineaire benadering in de parameters 

 

Deel 2 Lineaire benaderingsproblemen 

  • Beste benadering van vectoren in een lineaire deelruimte: Scheve en orthogonale basis in Rn; Orthogonalisatieprocedures; Beste benadering van vectoren 
  • Benadering van functies in deelruimtes: Metrische ruimte en afstand; Genormeerde ruimte en lengte; Unitaire ruimte en orthogonaliteit; Benadering in Euclidische ruimten 
  • Veeltermbenadering: Kleinste-kwadratenbenadering met veeltermen: Orthogonale veeltermen; Continue kleinste-kwadratenbenadering  
  • Benaderingen door middel van splines: Definitie en eigenschappen; B-spline basis; Bewerkingen op splines 
  • Discrete benadering op basis van meetdata: Opstellen van de benadering; Ruis en overfitting 

 

Deel 3 Data, grafen en eigenwaarden 

  • Grafen en eigenwaardenproblemen in data science: PageRank; Meest centrale knoop; Spectrale clustering; Partitionering van een graaf 
  • Numerieke methodes voor eigenwaardeproblemen: Methode van de machten; deelruimte-iteratie; QR-algoritme zonder en met shifts; Krylov methodes 

 

Deel 4 Niet-lineaire benadering 

  • Niet-lineaire benaderingsproblemen in de praktijk: Functies met niet-lineaire parameterafhankelijkheid; Diepe neurale netwerken 
  • Optimalisatiemethodes: Gradient descent method and stochastic gradient descent; Conjugate gradient method; Gauss-Newton methode; Leren uit data 
  • IJle representatie en benaderingen: Singuliere waardenontbinding: definitie en eigenschappen; algoritmes; Reductie van datasets en Principal Component-Analysis; Lagerangbenaderingen 

Studiemateriaal

Studiekost: 51-75 euro (De informatie over studiekosten zoals hier opgenomen is indicatief en geeft enkel de prijs weer bij aankoop van nieuw materiaal. Er zijn mogelijk ook e- en tweedehandskopijen beschikbaar. Op LIMO kan je nagaan of het handboek beschikbaar is in de bibliotheek. Eventuele printkosten en optioneel studiemateriaal zijn niet in deze prijs vervat.)

Cursustekst

Numerieke benadering met toepassing in datawetenschappen: oefeningen (B-KUL-H01P4a)

1.2 studiepunten : Practicum 20 Tweede semesterTweede semester

Inhoud

De oefenzittingen zijn programmeerzittingen in Matlab op basis van opgaven die verband houden met de inhoud van het hoorcollege. 

Studiemateriaal

Opdrachten gegeven tijdens de oefenzittingen.

Numerieke benadering met toepassing in datawetenschappen: practica (B-KUL-H01Z3a)

0.8 studiepunten : Opdracht 0 Tweede semesterTweede semester

Inhoud

Twee opdrachten, uit te voeren in Matlab en schriftelijk te rapporteren, waarbij een deelaspect uit de inhoud van het hoorcollege verder uitgediept wordt.   

Algemene doelstellingen: 

  • dieper inzicht in theorie verwerven 
  • ontwikkeling van een efficiënte Matlab implementatie 
  • ontwerp van nieuwe, gelijkaardige numerieke algoritmen aan deze gezien in de hoorcolleges 
  • schrijven van wetenschappelijk verslag 

Studiemateriaal

Opdracht verspreid via Toledo.

Toelichting werkvorm

De practica worden alleen of met 2 gemaakt. Bij elk practicum moet er een verslag geschreven worden. De beoordeling van de practica gebeurt op basis van dit verslag. 

Evaluatieactiviteiten

Evaluatie: Numerieke benadering met toepassing in datawetenschappen (B-KUL-H21P3a)

Type : Partiële of permanente evaluatie met examen tijdens de examenperiode
Evaluatievorm : Schriftelijk, Verslag

Toelichting

De evaluatie voor dit vak bestaat enerzijds uit de kwotering voor de practica en anderzijds uit de kwotering voor het examen.
Een student moet slagen op elk van deze twee onderdelen (practica, eindexamen) om in totaal te kunnen slagen. 

 

Toelichting bij herkansen

De evaluatie voor dit vak bestaat enerzijds uit de kwotering voor de practica en anderzijds uit de kwotering voor het examen. Een student moet slagen op elk van deze twee onderdelen (practica, eindexamen) om in totaal te kunnen slagen.

Bij niet slagen voor de practica in de juni-zittijd wordt een extra opgave voorzien.  Bij het slagen voor de practica in de juni-zittijd moet voor de herkansing van het vak geen nieuwe opgave gemaakt worden.

Bij het niet slagen voor het vak in de juni-zittijd moet het examen steeds opnieuw afgelegd worden.

ECTS Computer Vision (B-KUL-H02A5A)

4 ECTS English 30 Second termSecond term Cannot be taken as part of an examination contract
N. |  Proesmans Marc (substitute)

Aims

Computer vision or Image understanding is the 'art' of developing computerized procedures to extract relevant numerical and symbolic information from images. Not backed up by a single theory, we provide a structured overview of, and guidelines for, computer vision or image understanding strategies. With the recent succes of Neural Network based applications in Computer Vision, Deep Learning approaches will be discussed as well, next to more traditional approaches. 

Previous knowledge

Basic programming experience. Some mathematical background.

Is included in these courses of study

Onderwijsleeractiviteiten

Computer Vision: Lecture (B-KUL-H02A5a)

1.5 ECTS : Lecture 20 Second termSecond term
N. |  Proesmans Marc (substitute)

Content

Part I: Early and Mid-Level Vision

1.   Introduction to Computer Vision

2.   Basic Image Processing

  • Linear Filtering
  • Pyramids/Template Matching
  • Non-linear Filtering (median, bilateral filtering)
  • Morphology

3.   Feature Detection and Matching

  • Edges
  • Points/Patches
  • Fitting
  • Hough

4.   Grouping & Segmentation

  • Clustering (K-Means, Agglomerative Clustering, Mean Shift)
  • Spectral Clustering (Normalized Cuts, Graph cuts)
  • Deformable Contours (Active Contours, Dynamic Programming)

Part II: High-level Vision

5.   Introduction to Image Understanding

6.   Object Detection

  • Scanning/Sliding Window
  • Eigenfaces, HOG, LBP
  • Boosting (Intro, Haar-Cascade)

7.   Instance Recognition

  • Local Feature Matching
  • Bag of Words
  • Spatial Verification

8.   Deep Learning for Image Classification

  • Intro to Deep Learning
  • DCNN for Image Classifcation

Course material

Slides and references to online available textbooks and papers

Computer Vision: Project (B-KUL-H02K5a)

2.5 ECTS : Assignment 10 Second termSecond term
N. |  Proesmans Marc (substitute)

Content

Concepts presented in the lectures as well as complementary aspects are further discussed through hands-on experience.

Three assignments are planned. They all make use of the Python scripting language. 

  • The first assigment helps you to get acquainted with basic image processing steps required for further image analysis. 
  • The second assignment presents different (both traditional and DL) approaches to object detection and recognition.
  • The third assignment helps the student mastering DL approaches for image classification and segmentation

Course material

Python scripting language. Online references. 

Evaluatieactiviteiten

Evaluation: Computer Vision (B-KUL-H22A5a)

Type : Continuous assessment without exam during the examination period
Description of evaluation : Project/Product, Report
Type of questions : Open questions
Learning material : Computer

Explanation

The evaluation is based on the three assignments.

The first is an individual assignment and will be evaluated on the quality and originality of the submitted output (a video showing image processing results). 

The second and third assignment are group assignments. They will be evaluated on the quality of the submitted reports. 

The weighting of the different assignments is (20%, 40%, 40%). For the second and third assignment, peer assessment will be used to detect gross asymmetric contributions of group members. 

ECTS Natural Language Processing (B-KUL-H02B1A)

4 ECTS English 33 First termFirst term Cannot be taken as part of an examination contract

Aims

The course focuses on an in-depth understanding of methods and algorithms for building computer software that understands, generates and manipulates human language. We study the algorithms and models while introducing core tasks in natural language processing (NLP), including language modeling, syntactic analysis, semantic interpretation, machine translation, coreference resolution, discourse analysis, machine reading comprehension, question answering and dialogue modeling. We illustrate the methods and technologies with current applications in real world settings.

After following this course, the student has acquired an in-depth theoretical and practical understanding of contemporary machine learning models designed for processing human language and of the underlying computational properties of NLP models. The student will have learned how linguistic features can be modeled and automatically learned from data using deep learning techniques, to understand the inner workings of the machine learning models, and how we can make them computationally efficient.

Previous knowledge

This course focuses on the algorithms, mathematical modeling and machine learning methods for processing human language. We rely on a good understanding of the machine learning foundations. Hence there is the prerequisite to have successfully passed or to follow in parallel a machine learning course (e.g., Machine Learning and Inductive Inference - B-KUL-H02C1A, Principles of Machine Learning - B-KUL-H0E98A, Artificial Neural Networks and Deep Learning - B-KUL-H02C4A). Knowledge of the basics of linear algebra and of probability theory is required.

Students who also want to deepen their knowledge of the linguistic aspects of natural language processing are recommended to follow this advanced natural language processing course and the course Linguistics and Artificial Intelligence (B-KUL-H02B6A) in parallel.

Is included in these courses of study

Onderwijsleeractiviteiten

Natural Language Processing: Lecture (B-KUL-H02B1a)

3.5 ECTS : Lecture 20 First termFirst term

Content

1 Introduction

  • What is natural language processing (NLP)? What is natural language understanding? What is natural language generation?
  • Challenges:
    • Ambiguity, uncertainty and incompleteness of language
    • Lack of annotated training data
    • The symbolic system of language versus the continuous nature of current representations and their integration
    • The role of memory when processing language
  • Word and character embeddings
  • Evaluation

 

2 Sequential Tagging: Part 1

  • Token tagger versus sequence tagger
  • Hidden Markov model
  • Maximum entropy model
  • Conditional random field (CRF)
  • Feature functions
  • Objective functions for training
  • Decoding during inference: greedy search, beam search and Viterbi algorithm
  • Case study: Part-of-speech tagging

 

3 Sequential Tagging: Part 2

  • Sequential modeling with a recurrent neural network (RNN)
  • Learning long-term dependencies with a long short-term memory network (LSTM)
  • Encoder-decoder architecture for sequence-to-sequence labeling
  • Stochastic gradient optimization
  • Multi-task learning and parameter sharing
  • Case studies: Named Entity Recognition (NER) and Relation Extraction

 

4 Language Modeling and Attention

  • N-gram language models, smoothing, discounting and interpolation
  • Recurrent neural network (RNN) for language modeling
  • Transformer architecture for language modeling
  • Attention mechanisms: cross-attention, self-attention and multi-head attention

 

5 Foundation Models and Fine-tuning

  • Use of language models in downstream NLP tasks: pretraining, fine-tuning and (soft) prompting
  • Parameter efficient training
  • Parameter efficient fine-tuning
  • Low rank adaptation
  • Case studies: Cross-lingual transfer

 

6 Recognition of Tree Structures

  • Recursive neural network
  • Graph based parsing
  • Transition based parsing
  • Algorithms of dynamic programming for efficient decoding
  • Case studies: Constituent parsing and dependency parsing of a sentence

 

7 Recognition of Complex Graph Structures

  • Revisiting sequential modeling: CRF and RNN
  • Graph convolutional neural network
  • Modeling multi-relational information in graph convolutional neural network
  • Iterative inference over memory
  • Case studies: Semantic role labeling and abstract meaning representation parsing

 

8 Semantic Parsing

  • Coarse-to-fine decoding for neural semantic parsing
  • Span based compositional generalization
  • Induction of latent structures for semantic parsing
  • Language-to-code modeling
  • Case studies: mapping of sentences to logical form, lambda calculus and denotation

 

9 Coreference Resolution

  • The Winograd scheme and commonsense reasoning
  • Mention pair and mention ranking models
  • Global optimization based on integer linear programming
  • Enforcing coreference cluster consistencies in deep learning models

 

10 Neural Machine Translation

  • Encoder-decoder architecture revisited (e.g., RNN, transformer based)
  • Attention models revisited
  • Adversarial training
  • Advanced decoding: stochastic algorithms based on sampling
  • Improvements and alternative architectures that deal with limited parallel training data

 

11 Spatial and Temporal Recognition

  • Spatial relation recognition, temporal expression recognition and normalization, temporal relation recognition
  • Spatial and temporal reasoning:
    • Structured learning and inference with integer linear programming (ILP)
    • Markov logic networks, prediction of relative and absolute timelines
    • Integration of temporal and spatial reasoning in objective functions used for training
  • Case studies: Prediction of relative and absolute timelines, prediction of spatial positions in a 2D or 3D physical space, towards spatio-temporal recognition

 

12 Question Answering and Machine Reading Comprehension

  • Revisiting neural representations of questions and text
  • Variational autoencoder for representing a sentence
  • Neural memory network
  • Case studies: Text based question answering, natural language inference when processing discourse

 

13 Conversational Dialogue Systems and Chatbots

  • Task oriented dialog agents: Rule based versus neural based approaches
  • Chatbots:
    • End-to-end sequence-to-sequence neural models (RNN and transformer architectures)
    • Generative hierarchical neural models
    • Role of reinforcement learning
    • Controlled response generation and decoding (e.g., diversity, personalization)

 

Course material

Handbooks

Jacob Eisenstein. Introduction to Natural Language Processing. 2019. MIT Press.

Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing. https://www.jair.org/index.php/jair/article/download/11030/26198/

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. 2016. MIT Press.

  
+ recent articles: e.g., of the proceedings of the Meetings of the ACL, AAAI, NeurIPS.

Format: more information

Lectures.

Is also included in other courses

G0D25A : Natural Language Processing

Natural Language Processing: Exercises (B-KUL-H00G0a)

0.5 ECTS : Practical 13 First termFirst term

Content

  • Exercises on sequential tagging
  • Exercises on language modeling, meaning representations and attention mechanisms
  • Exercises on recognition of graph structures and semantic parsing
  • Exercises on structured learning and prediction, integrating integer linear programming constraints, and induction of latent structures
  • Exercises on machine translation and decoding
  • Exercises on temporal and spatial recognition and reasoning, and machine reading
  • Hands-on exercises on deep learning in natural language processing

Is also included in other courses

G0D25A : Natural Language Processing

Evaluatieactiviteiten

Evaluation: Natural Language Processing (B-KUL-H22B1a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions, Closed questions
Learning material : Calculator, Course material

Explanation

Open book written exam featuring a mixture of theory and exercise questions.

ECTS Machine Learning and Inductive Inference (B-KUL-H02C1A)

4 ECTS English 35 First termFirst term Cannot be taken as part of an examination contract

Aims

This course will familiarise the students with the domain of machine learning, which concerns techniques to build software that can learn how to perform a certain task (or improve its performance on it) by studying examples of how it has been accomplished previously, and in a broader sense the discovery of knowledge from observations (inductive inference).
After following this course, students will:

  • have a basic understanding of the general principles of learning
  • have an overview of the existing techniques for machine learning and data mining
  • understand how these techniques work, and why they work
  • be able to implement programs that learn or exhibit adaptive behavior, using these techniques
  • be up-to-date with the current state of the art in machine learning research
  • be able to contribute to contemporary machine learning research

Previous knowledge

Students should be familiar with:

  • algorithms and programming
  • some elements from higher mathematics, probability theory and statistics
  • predicate logic

 

 

Introductory courses on these topics (at the Bachelor level) suffice.

 

Is included in these courses of study

Onderwijsleeractiviteiten

Machine Learning and Inductive Inference: Lecture (B-KUL-H02C1a)

3 ECTS : Lecture 20 First termFirst term

Content

1. introduction to machine learning, connections with other subjects
2. general principles of learning:
- concept learning, version spaces
- evaluation of learning algorithms
- theory of learnability
- representation of inputs and outputs of learning algorithms
3. specific learning approaches:
- decision trees
- rules, association rules
- instance based learning
- clustering
- neural networks
- support vector machines
- Bayesian learning
- genetic algorithms
- ensemble methods (bagging, boosting, ...)
- reinforcement learning
- inductive logic programming
 

Course material

Course Text
Lecture slides

Format: more information

Ten lectures of 2 hours each.
 

Machine Learning and Inductive Inference: Exercises (B-KUL-H00G6a)

1 ECTS : Practical 15 First termFirst term

Content

Exercises are made on the subjects discussed during the lectures. These are mostly pen-and-paper exercises where students gain insight in the workings of learning algorithms by manually mimicking the computations of certain learning algorithms, graphically describing the result of a learning algorithm (by drawing decision surfaces), etc. There are also exercises on evaluation of machine learning models and algorithms.

Course material

  • A list of exercises.
  • Solutions are made available on Toledo.

Format: more information

Students try to independently solve the exercises during some time. A teaching assistant provides help where necessary, and discusses the solution afterwards.

Evaluatieactiviteiten

Evaluation: Machine Learning and Inductive Inference (B-KUL-H22C1a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Multiple choice, Open questions, Closed questions
Learning material : Calculator, List of formulas

Explanation

The exam consists of questions about the theory and exercises. A formula sheet can be consulted during the exam. 

If the evaluation shows that the student does not meet one or more objectives of the course, the global result may differ from a weighted average of the parts.

ECTS Genetic Algorithms and Evolutionary Computing (B-KUL-H02D1A)

4 ECTS English 70 First termFirst term Cannot be taken as part of an examination contract

Aims

The student understands, recognizes, can explain why, and can give examples of settings in which evolutionary algorithms are or are not a viable solution approach. They can pinpoint, explain, and analyze the strengths and weaknesses of evolutionary algorithms both in general and in specific problem instances.

The student can list, describe, explain, analyze, and implement in the Python programming language the common basic components of evolutionary algorithms (objective function, representation, selection operators, variation operators, and elimination operators). Additionally, they can propose, develop, and implement new problem-specific, adapted components as required in a specific application. The student can list, describe, explain, analyze, and implement in the Python programming language advanced components of evolutionary algorithms that represent some of its characteristic strengths such as diversity promotion mechanisms, multi-objective optimization, and local search operators. The student furthermore can describe, analyze, and reason about the interaction between the various common and advanced components of evolutionary algorithms. In particular, they can analyze and explain the role of the various hyperparameters that can influence the strength and nature of these interactions.

The student can design and implement in Python a full evolutionary algorithm pipeline well adapted to a specific problem. Moreover, the student can put their opinions, arguments, and reasoning about the aptness of an evolutionary algorithm design (i.e. the employed components and their interactions) into both convincing writing and oral communication.

The student can implement in the Python programming language a complete evolutionary algorithm pipeline, including objective function, representation, selection operators, variation operators, elimination operators, local search operators, diversity promotion mechanisms, and multi-objective optimization techniques from scratch. They can recognize, interpret, analyze, and resolve common problems arising in evolutionary algorithms, such as loss of diversity, misalignment of the selective pressure, misalignment of exploration versus exploitation, and computational bottlenecks.

Previous knowledge

Basic undergraduate courses in informatics (programming, algorithms, data structures) and mathematics (statistics, calculus). The following specific items are assumed to be known:

Informatics

  • Good programming skills in Python (or Julia)
  • Elementary data structures (arrays, lists, matrices)
  • Graphs (definition, basic graph algorithms like shortest path computation)
  • Elementary theoretical computer science (computational problems, P vs. NP) is a bonus

Calculus

  • Multivariate functions
  • Minimization and maximization
  • Partial and full derivatives
  • Gradients of multivariate functions

Statistics

  • Normal distribution
  • Random variables
  • Probability density function, cumulative density function
  • Mean, variance, standard deviation

Is included in these courses of study

Onderwijsleeractiviteiten

Genetic Algorithms and Evolutionary Computing: Lecture (B-KUL-H02D1a)

1.8 ECTS : Lecture 20 First termFirst term

Content

High-level contents and materials

  • Lecture 1: Introduction
  • Lecture 2: Problems, representation, and variation
  • Lecture 3: Population management
  • Online module 1: Local search operators 
  • Online module 2: Multiobjective optimization and diversity promotion
  • Online case study modules: Hands-on programming exercises

Detailed contents

Basics of evolutionary algorithms

  • Exploration versus exploitation
  • Computational and optimization problems
  • Objective function
  • Representation
  • Constraints
  • Variation operators
  • Selection and elimination operators
  • Hyperparameter self-adaptivity

Local search operators

  • Steepest descent
  • Monte Carlo sampling
  • k-opt

Multi-objective optimization and diversity promotion

  • Crowding
  • Island model
  • Fitness sharing
  • Scalarization (fixed tradeoff)
  • Pareto front

Course material

Study cost: 51-75 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Slides, online videos, textbook (e-book).

Genetic Algorithms and Evolutionary Computing: Exercises (B-KUL-H00H1a)

0.6 ECTS : Practical 10 First termFirst term

Content

The exercises consists of four guided sessions during which (most of) the group phase of the project is executed. For the group phase you will be assigned to balanced groups of 3-5 students. Attendance to the exercise sessions is mandatory, and non-participation results in a final mark of NA.

In the four exercise sessions, a basic evolutionary algorithm will be designed. The content of the sessions are:

  • Design of an elementary evolutionary algorithm
  • Computer implementation of an evolutionary algorithm in Python
  • Experimentation with the algorithm and reporting
  • Reading reports and providing peer feedback to other groups

Genetic Algorithms and Evolutionary Computing: Project (B-KUL-H08M3a)

1.6 ECTS : Assignment 40 First termFirst term

Content

The students undertake a two-phase project. The first phase is a group work in which the students analyze a model problem and design, implement, and test an evolutionary algorithm in the Python programming language. This phase is concluded by a peer feedback assignment in which the students analyse one or more designs from other teams and provide feedback on them. The second phase is performed individually by each student, in which they analyze the results from their group phase, and based on the acquired insights, design, implement, and test improved variation and local search operators, selection mechanisms, diversity promotion schemes, among others. The students report the results of their analysis (results, computational performance, strengths and weaknesses, among others) via two reports, one for each phase. 

The reports will be discussed on the oral exam and, along with additional general questions, constitute the majority of the grade for this course. See the evaluation section for more details.

Evaluatieactiviteiten

Evaluation: Genetic Algorithms and Evolutionary Computing (B-KUL-H22D1a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Oral, Participation during contact hours, Take-Home
Type of questions : Open questions
Learning material : None

Explanation

The evaluation is oral without written preparation. The exam consists of a short discussion of the project and open theoretical questions about the course with the project as potential case study.

The score of the exam is the sum of the score of the group phase of the project, the individual phase of the project, and the exam.

If the student fails to participate in one of the components of the project (exercises, peer feedback, individual programming, individual report), the outcome of the exam is NA.

Information about retaking exams

The retake exam consists of an updated project assignment for the take-home exam and an oral exam without written preparation. The setup of the exam and scoring is the same as in the first examination period, except that the group phase must be completed individually if its previous outcome was NA. The updated assignment will be uploaded after the closing of the second examination period to Toledo. The deadline of the project will be at least one week before the opening of the third examination period.

The score of the exam is determined according to the same modalities as in the first exam attempt.

ECTS Support Vector Machines: Methods and Applications (B-KUL-H02D3A)

4 ECTS English 30 Second termSecond term Cannot be taken as part of an examination contract

Aims

After a brief introduction to the basics of statistical decision theory and pattern recognition this course focuses on methods of support vector machines for classification and regression. Support vector machine models make use of kernel functions (including e.g. linear, polynomial, radial basis function and spline kernels). In general it relates to several kernel based learning methods. The solutions typically follow from solving convex optimisation problems. Besides problems of supervised learning methods for unsupervised learning such as kernel principal component analysis are discussed as well. Support vector models are typically able to learn and generalise in very high dimensional input spaces. In this course the methods will be illustrated by examples and applications in datamining, bioinformatics, biomedicine, text-mining, finance and others.

Previous knowledge

Basic knowledge of linear algebra.

Identical courses

H02D3B: Support Vector Machines: Methods and Applications

Is included in these courses of study

Onderwijsleeractiviteiten

Support Vector Machines: Methods and Applications: Lecture (B-KUL-H02D3a)

3 ECTS : Lecture 20 Second termSecond term

Content

- Introduction and motivation
- Basics of statistical decision theory and pattern recognition
- Basics of convex optimisation theory, Karush-Kuhn-Tucker conditions, primal and dual problems
- Maximal margin classifier, linear SVM classifiers, separable and non-separable case
- Kernel trick and Mercer theorem, nonlinear SVM classifiers, choice of the kernel function, special kernels suitable for textmining
- Applications: classification of microarray data in bioinformatics, classification problems in biomedicine
- VC theory and structural risk minimisation, generalisation error versus empirical risk, estimating the VC dimension of SVM classifiers, optimal tuning of SVMs
- SVMs for nonlinear function estimation
- Least squares support vector machines, issues of sparseness and robustness, Bayesian framework, probabilistic interpretations, automatic relevance determination and input selection, links with Gaussian processes and regularisation networks, function estimation in RKHS.
- Applications: time-series prediction, finance  
- Kernel versions of classical pattern recognition algorithms, kernel Fisher discriminant analysis
- Kernel trick in unsupervised learning: kernel based clustering, SVM and kernel based density estimation, kernel principal component analysis, kernel canonical correlation analysis
- Applications: datamining, bioinformatics
- Methods for large scale data sets, approximation to the feature map (Nystrom method, Random Fourier features), estimation in the primal
- SVM extensions to recurrent models and control; Kernel spectral clustering; Deep learning and kernel machines; attention and transformers from a kernel machines perspective.

 


(10 lectures (2 hours) + 3 computer exercise sessions)

 

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

The course material is largely based on the textbook
J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle,  Least Squares Support Vector Machines, World Scientific, Singapore, 2002 (ISBN 981-238-151-1)
Related books:
Cristianini N., Shawe-Taylor J., An introduction to support vector machines,  Cambridge University Press, 2000.
Schoelkopf B., Burges C., Smola A., Advances in Kernel Methods: Support Vector Learning,  MIT Press, Cambridge, 1998.
Schoelkopf B., Smola A., Learning with Kernels,  MIT Press, Cambridge, 2002
Vapnik V., Statistical learning theory, John Wiley, New-York, 1998.

Support Vector Machines: Methods and Applications: Exercises (B-KUL-H00H3a)

1 ECTS : Practical 10 Second termSecond term

Format: more information

3 computer exercise sessions

Evaluatieactiviteiten

Evaluation: Support Vector Machines: Methods and Applications (B-KUL-H22D3a)

Type : Exam during the examination period
Description of evaluation : Oral, Written
Type of questions : Open questions
Learning material : Course material

Explanation

Individually written report about the exercise sessions, with additional oral discussion.

ECTS Bio-informatics (B-KUL-H02H6B)

4 ECTS English 20 Second termSecond term Cannot be taken as part of an examination contract

Aims

The course describes data-mining methods in bioinformatics. The biological content is kept to a bare minimum. The focus is on probabilistic models (sequence alignment as dynamic programming, Expectation-Maximization, Markov Chain Monte Carlo methods). The content is very relevant to data-mining applications outside bioinformatics. The emphasis lies on the basic concepts underlying probabilistic methods and how they are transformed into practical applications.

The first objective of the course is for students to acquire a coherent understanding of the main probabilistic models, optimization criteria, and optimization algorithms used in bioinformatics:
* Models: generative models, hidden Markov models, breakpoint change models
* Estimation and inference: maximum likelihood, maximum a posteriori, Bayesian inference
* Algorithms: dynamic programming, Expectation-Maximization, Markov Chain Monte Carlo, Gibbs sampling.

Also, through the study of the diverse applications of such models to biological problems, the course aims at developing the capacity of the student to translate simple biological problems into data analysis problems using probabilistic models. Finally, students will also develop the capacity to derive appropriate algorithms for the optimization of a given probabilistic model.

Previous knowledge

Actual fluency in calculus and with the basic concepts of probability theory and statistics.

Is included in these courses of study

Onderwijsleeractiviteiten

Bio-informatics (B-KUL-H02H6a)

4 ECTS : Lecture 20 Second termSecond term

Content

1. Introduction to molecular biology
    - DNA
    - RNA
    - Proteins
 
2. Sequence alignment
    - Dynamic programming
    - Global and local alignment
    - BLAST
 
3. Introduction to Bayesian statistics
    - The Cox-Jaynes axioms
    - Maximum likelihood, maximum a posteriori, and Bayesian inference
    - Dirichlet distributions and pseudocounts

4. Hidden Markov Models  (HMMs)
      - Viterbi decoding
      - Forward-backward algorithm
      - HMM estimation with known paths
      - Viterbi learning
      - Baum-Welch algorithm

5. Applications of HMMs
    - Modeling protein families
    - Gene prediction
 
6. Expectation-Maximization for clustering and motif finding
    - The EM algorithm
    - EM for clustering
    - EM for motif finding
 
7. Gibbs sampling for motif finding and biclustering
    - Markov Chain Monte Carlo methods
    - Gibbs sampling
    - Motif finding

Course material

R. Durbin, A. Krogh, G. Mitchinson, S. Eddy, "Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids", Cambride University Press, 1999.

Powerpoint slides

Handwritten notes

Evaluatieactiviteiten

Evaluation: Bio-informatics (B-KUL-H22H6b)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions
Learning material : None

Explanation

The exam consists of three questions:
* 1 theoretical question (e.g., describe one of the algorithms of the course)
* 1 theoretical exercise (e.g., derive a new relationship relevant to the material of the course)
* 1 practical exercise (e.g., apply one of the algorithms to a simple case)

ECTS Bedrijfservaring: Wiskundige ingenieurstechnieken / Industrial Experience: Mathematical Engineering (B-KUL-H02X6A)

3 studiepunten Nederlands 60 Eerste semesterEerste semester Uitgesloten voor examencontract Uitgesloten voor creditcontract
Meerbergen Karl (coördinator) |  De Moor Bart |  Meerbergen Karl

Doelstellingen

Het opleidingsonderdeel bedrijfservaring is bedoeld als kennismaking met de werkomgeving van een bedrijf. In tegenstelling tot industriële stages is het niet de bedoeling een concreet industrieel project volledig uit te werken, maar een kleine opdracht die aanleunt bij de opleiding uit te voeren in een reële bedrijfsomgeving.

Begintermen

Studenten die de Master Wiskundige Ingenieurstechnieken mogen aanvatten.

Onderwijsleeractiviteiten

Bedrijfservaring: Wiskundige ingenieurstechnieken / Industrial Experience: Mathematical Engineering (B-KUL-H02X6a)

3 studiepunten : Stage 60 Eerste semesterEerste semester

Inhoud

De stage omvat een werkverblijf met een minimale duur van 4 weken in een bedrijf. Gedurende deze periode zal de student activiteiten uitoefenen die voldoen aan de hoger gestelde doelstellingen. Hij zal hierover achteraf schriftelijk en mondeling rapporteren

Toelichting werkvorm

Meer informatie over de praktische kant van de stage verwijzen we naar de website van de docent

Evaluatieactiviteiten

Evaluatie: Bedrijfservaring: Wiskundige ingenieurstechnieken / Industrial Experience: Mathematical Engineering (B-KUL-H22X6a)

Type : Permanente evaluatie zonder examen tijdens de examenperiode
Evaluatievorm : Verslag, Presentatie
Vraagvormen : Open vragen
Leermateriaal : Geen

Toelichting

De evaluatie gebeurt op basis van de volgende delen:

  • Verslag (10-15 blz.): na de stage maakt de student (in overleg met het bedrijf) onmiddellijk een rapport op. Dit wordt aan de stagecoördinator bezorgd.
  • Logboek: de stagair maakt dagelijks een korte beschrijving van de activiteiten van de dag
  • Presentatie: de student stelt mondeling kort zijn stage voor op een overeengekomen tijdstip.
  • Evaluatieformulier ingevuld door het bedrijf

Het stagerapport bestaat uit zes delen:

  • Administratieve gegevens: naam van de student, studiejaar, naam en adres van het bedrijf, naam van de contactpersoon in het bedrijf, naam van de stagecoördinator en de stageperiode.
  • De inleiding situeert het bedrijf: het (hoofd)product van het bedrijf, de plaats van het bedrijf in zijn sector.
  • Het hoofdgedeelte start met een korte, precieze beschrijving van de stageopdracht en plaatst dit in het geheel van het bedrijf. Daarna volgt een wat uitgebreidere beschrijving samen met de eventuele resultaten. Tracht steeds aan te geven wat uw taak precies geweest is.
  • Het derde deel geeft de belangrijkste besluiten weer: de behaalde resultaten, datgene wat de stage heeft bijgebracht (oa. competenties),...
  • Daarna een overzicht van het verband tussen de stage en reeds gevolgde opleidingsonderdelen.
  • Het logboek dat de werkzaamheden dag per dag kort beschrijft.

Toelichting bij herkansen

De evaluatie betreft de mogelijkheid tot een betere rapportering (verslag en presentatie). De stage zelf kan niet opnieuw worden gedaan.

ECTS Nonlinear Systems (B-KUL-H03D9A)

6 ECTS English 38 Second termSecond term Cannot be taken as part of an examination contract
Suykens Johan (coordinator) |  Feppon Florian |  Suykens Johan

Aims

The dynamics of many processes that occur in the real world are dominated by non-linear factors and are increasingly exploited in applications. This course wishes to provide insight into non-linear phenomena and complex forms of behaviour that occur in many problems in science and technology. It will be shown that the mathematical models for such phenomena share many characteristics, and it will be indicated how these mathematical models can be analyzed using analytical techniques and numeral methods and software.

Previous knowledge

Skills: the student should be able to analyze, synthesize and interpret
Knowledge: analysis, differential equations (e.g. Technical Mathematics), Numeral Mathematics, Linear System Theory
Preliminary conditions: Analysis (e.g. H01A0 and H01A2), differential equations (e.g. Technical mathematics), Numeral mathematics (e.g. H01D8A), System theory and control theory (H01M8A)

Identical courses

H0S11A: Niet-lineaire systemen

Onderwijsleeractiviteiten

Nonlinear Systems: Lecture (B-KUL-H03D9a)

3 ECTS : Lecture 18 Second termSecond term

Content

The following points will be illustrated by means of concrete examples from different disciplines.
1) Introduction
From linear to non-linear. Historical overview and examples.
2) Dynamic systems
* Study of one-dimensional differential systems:
- with straight line as state space: balance points and stability
- with the circle as state space: uniform and non-uniform oscillators, synchronization
* Study of two-dimensional differential systems
- Phase plane and phase portraits:
balance points, characterizing the nature of balance points through linearization and its conditions.
Special characteristics of phase portraits in conservative and reversible systems.
- Limit cycles and conditions for their existence.
Gradient systems. Lyapounov functions.
Poincaré-Bendixon theorem.
- Strange attractors (chaotic behaviour)
- Liénard systems and relaxation oscillators. Weak non-linear oscillators and pertubation theory.
3) Bifurcation analysis
- Concepts from bifurcation theory for parameter-dependent non-linear systems: bifurcations and their normal forms (including Hopf-bifurcation, homoclinic bifurcation), catastrophe theory
- Coupled oscillators: synchronization, quasi periodicity
- Poincaré pictures
4) Chaos theory
- Deterministic chaos, chaos in continuous time systems and in discrete time systems
- Lorenz' equation, discrete Lorenz' picture
- Logistic picture
- Roads to chaos: Lyapounov exponent, fractal dimension
5) Spatial pattern formation
- Pattern formation in cellular automatics
- Turing structures in reaction-diffusion problems
- Pattern formation in hydrodynamic problems
6) Numerical methods for continuation and bifurcation analysis
- Numerical continuation of solution branches
- Numerical methods for the calculation of periodical solutions and their stability (Monodromy matrix), calculating the Lyapounov exponents.
- Functionality and use of software packages for the analysis of dynamic systems and bifurcation analysis.

Course material

Study cost: 51-75 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Handbook/articles and literature/toledo.

Nonlinear Systems: Exercises and Laboratory Sessions (B-KUL-H03E0a)

1 ECTS : Practical 20 Second termSecond term

Content

Exercises and practical sessions with the lecture: Non-linear systems.

Course material

Handbook/articles and literature/toledo.

Nonlinear Systems: Project (B-KUL-H09N0a)

2 ECTS : Assignment 0 Second termSecond term

Evaluatieactiviteiten

Evaluation: Nonlinear Systems (B-KUL-H23D9a)

Type : Exam during the examination period
Description of evaluation : Oral, Written
Type of questions : Open questions
Learning material : Course material, Computer

ECTS System Identification and Modeling (B-KUL-H03E1B)

4 ECTS English 39 First termFirst term Cannot be taken as part of an examination contract
De Moor Bart (coordinator) |  De Moor Bart |  N.

Aims

Estimating mathematical models, starting from measured data, is an important step in many engineering methods. This course contains a number of important methods and foundations for linear system identification and modeling. We discuss topics such as choosing a good model structure, appropriate parametrizations, criteria for model selection and statistical properties of the obtained estimates. The course deals with least squares estimation, prediction error techniques, state space models and realization theory. The emphasis is on methods that offer a good generalization. The methods are illustrated with many practical examples and applications.

Previous knowledge

Skills: the student should be able to analyze, synthesize and interpret
 

Knowledge:

  • Necessary: calculus, applied linear algebra, probability theory and statistics, system theory
  • Useful, but not necessary: control theory

 

Detailed list of prerequisites:


1. Calculus: Analyse 1 (H01A0B), Analyse 2 (H01A2B)

  • Logic reasoning and mathematical proofs
  • Functions of real numbers, e.g., trigonometric, exponential, logarithmic
  • Functions of vectors
  • Differentiation and integration of univariate and multivariate functions
  • Partial derivatives
  • Complex numbers: addition, multiplication, powers of complex numbers
  • Vector spaces, gradient
  • Analytic geometry: Cartesian coordinates and polar coordinates
  • Differential equations: set up and solve linear differential equations and sets of differential equations
  • Taylor series
  • Optimization problems: formulate, solve and interpret, with equality and inequality constraints, method of Lagrange
  • Difference equations: solve linear difference equations and sets of difference equations

 

2. Applied linear algebra: Toegepaste Algebra (H01A4B); David Lay, “Linear Algebra and its Applications"

  • Familiarity with concepts from linear algebra in higher dimensions: vector spaces, linear dependence, orthogonality
  • Matrix computations: addition, multiplication with scalar, product of matrices, inverse of a matrix
  • Determinant
  • Partitioned matrices
  • Vector spaces: subspaces, linear transformations, basis, dimension, orthogonal complement of subspaces, orthogonal projection
  • Rank, column space, row space, null space of a matrix
  • Eigenvalue decomposition: characteristic polynomial, Cayley-Hamilton theorem, similar matrices
  • Singular value decomposition, QR factorization
  • Recognize and solve least squares problems
  • Pseudo-inverse of a matrix and relation to least squares
  • Algebraic models for engineering problems: Setting up a set of linear equations, processing of experimental results, analyzing autonomous systems and vibrations as an eigenvalue problem, computing the response of linear time-invariant discrete-time systems, dimensional reduction by means of the singular value decomposition
  • Use MATLAB to do matrix computations

 

3. Probability Theory and Statistics: Kansrekenen en statistiek (H01A6A)

  • Basic principles of probability theory: random variables, probability distributions
  • Variance, standard deviation, covariance, correlation
  • Estimation of parameters
  • Confidence intervals
  • Regression analysis

 

4. System Theory: Systeemtheorie en regeltechniek (H01M8A)

  • Basics about modeling mechanical, electrical, thermal and hydraulic systems
  • Block diagrams
  • Convolution, Laplace transform and Z transform (and their inverse)
  • Fourier series
  • Linear time invariant systems
  • Impulse response and transfer function
  • Poles and zeros of a system
  • Stability
  • State-space representation
  • Analysis of continuous-time and discrete-time systems in the time domain and in the frequency domain
  • Modeling and linearization
  • Discretization of continuous-time systems

Identical courses

H0S14A: Systeemidentificatie en modellering

Onderwijsleeractiviteiten

System Identification and Modeling : Lecture (B-KUL-H03E1a)

2 ECTS : Lecture 26 First termFirst term
De Moor Bart |  N.

Content

1. Introduction to System Identification and Modeling

  • data science, tsunami of data
  • dynamical models
  • machine learning vs system identification
  • mathematical modeling cycle = system identification loop
  • cases
  • more examples
  • interesting books

2. Linear Algebra for System Identification and Modeling

  • vectors and matrices
  • the singular value decomposition
  • eigenvalue problems

3. Optimization and Least Squares

  • optimization: unconstrained and constrained
  • ordinary least squares
  • weighted least squares
  • total least squares
  • recursive least squares

4. Models for Dynamical Systems

  • dynamical systems
  • system identification
  • misfit vs latency
  • commonly used models
  • state space models

5. System Identification by Least Squares

  • identification of an AR model
  • identification of an ARX model
  • other cases that reduce to ARX identification
  • recursive least squares in system identification

6. Prediction Error Methods

  • identification problem
  • prediction error
  • cost function
  • parameterizations
  • persistency of excitation
  • statistical properties
  • properties of identified transfer functions
  • system not in model set
  • model structure validation
  • frequency domain interpretation
  • preprocessing of data
  • user choices
  • validation

7. Realization Theory

  • realization of input-output state space model from impulse response
  • realization of autonomous system from output 
  • application: direction of arrival

8. Balanced Model Order Reduction

  • controllability and observability
  • energy interpretation of controllability and observability
  • controllability and observability Gramians
  • balanced realization
  • balanced model order reduction
  • application

Course material

The digital version of the course slides is provided in Toledo.

System Identification and Modeling : Exercises and Laboratory Sessions (B-KUL-H03E2a)

1 ECTS : Practical 13 First termFirst term
De Moor Bart |  N.

Course material

The assignments and instructions are provided in Toledo.

System Identification and Modeling : Project (B-KUL-H09N1a)

1 ECTS : Assignment 0 First termFirst term
De Moor Bart |  N.

Evaluatieactiviteiten

Evaluation: System Identification and Modeling (B-KUL-H23E1b)

Type : Exam during the examination period
Description of evaluation : Oral
Learning material : Course material

ECTS Optimization (B-KUL-H03E3A)

6 ECTS English 50 First termFirst term Cannot be taken as part of an examination contract

Aims

The course gives insight into the mathematical formulation of optimization problems and deals with advanced methods and algorithms to solve these problems. The knowledge of the possibilities and shortcomings of these algorithms should lead to a beter understanding of their applicability in solving concrete engineering problems. In the course, an overview of existing software for optimization will also be given, this software will be used in the practical exercise sessions. The student learns to select the appropriate solving methods and software for a wide range of optimization problems and learns to correctly interpret the results.

The following knowledge and skills will be acquired during this course:

  • The student will be able to formulate a mathematical optimization problem starting from a concrete engineering problem.
  • The student will be able to classify optimization problems into appropriate categories (e.g., convex vs. non-convex problems).
  • The student will be familiar with different optimization strategies and their properties, and will hence be able to decide which strategy to use for a given optimization problem.
  • The student will be able to formulate the optimality conditions for a given optimization problem.
  • The student will have a profound understanding of a wide variety of optimization algorithms and their properties, and will be able to apply the appropriate algorithms for a given optimization problem.
  • The student will be familiar with state-of-the-art optimization software packages, and will be able to use these in an efficient manner.

Previous knowledge

Skills: the student should be able to analyze, synthesize and interpret.
Knowledge: Analysis, Numerical mathematics, Numerical linear algebra.

Identical courses

H0S15A: Optimalisatie

Is included in these courses of study

Onderwijsleeractiviteiten

Optimization: Lecture (B-KUL-H03E3a)

4 ECTS : Lecture 30 First termFirst term

Content

1. Introduction
- a number of motivating examples (control, fitting, planning)
- mathematical modelling of optimization problems
- the importance of convexity
- classification of optimization problems
2. Algorithms for continuous optimization without constraints
- the two basic strategies: line search or trust region techniques
- gradient-based techniques: the steepest gradient and the added gradient method
- Newton and quasi-Newton techniques
- special methods for non-linea least square problems
3. Algorithms for continuous optimization with constraints
- the KKT-optimization conditions
- algorithms for linear problems: simplex-method and primal-dual interior point method
- algorithms for quadratic problems: active-set technique and interior point method
- convex optimization: formulation, the concept duality, algorithms
- general non-linear optimization (penalizing and barrier techniques, connection to interior point algorithms)

4. Introduction to global optimization methods
- deterministic methods (branch and bound, ...)
- stochastic and heuristic methods (Monte Carlo methods, simulated annealing, evolutionary algorithms, swarm-based algorithms,...)

5. Software
- discussion of the possibilities of the most current optimization software-packages
- sources on the internet: the Network Enabled Optimization Server

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

- Numerical Optimization, J. Nocedal and S. Wright, Springer, New York, 1999.
- Optimization Software Guide, J. Moré and S. Wright, SIAM, Philadelphia, 1993.

Is also included in other courses

H04U1C : Optimization of Mechatronic Systems

Optimization: Exercises and Laboratory Sessions (B-KUL-H03E4a)

2 ECTS : Practical 20 First termFirst term

Content

Exercises and lab sessions with the course Optimisation

Evaluatieactiviteiten

Evaluation: Optimization (B-KUL-H23E3a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions

Explanation

- part I, theory (closed-book with use of formulary) 
- part II, exercises (Open-book on computer; example programs are available)

ECTS Wavelets with Applications in Signal and Image Processing (B-KUL-H03F7A)

6 ECTS English 32 First termFirst term Cannot be taken as part of an examination contract

Aims

The student understands the terminology of the literature on wavelets. He/she understands the key concepts of wavelet theory and their applications in signal and image processing.

The student knows how to design wavelets with specified desired properties.

The student can explain the relationship between the theoretical properties of wavelets and the impact of these properties on the results of a specific application. 

The student can utilize wavelets in an application and  can describe and analyze his implementation.

The student can comprehend recent scientific articles on the topic of wavelets or applications of wavelets. He can describe and comment on such papers in his own words and form an opinion based on scientific arguments.

Previous knowledge

The student is familiar with elementary linear algebra operations, such as matrix operations and the eigenvalue decomposition.

The student is familiar with the basic principles of Fourier transforms and can reason about the representation of a function in the time domain and the frequency domain.

Knowledge of elementary system theory, such as impulse responses and linear time invariant filters, is certainly helpful but it is not required.

Onderwijsleeractiviteiten

Wavelets with Applications in Signal and Image Processing: Lecture (B-KUL-H03F7a)

3 ECTS : Lecture 20 First termFirst term

Content

  • 6 lectures are used to introduce the theory of wavelets. The focus of these lectures lies on the insights and the how-and-why of wavelets. This complements the course notes, which contain a more complete and in-depth treatment of wavelets, but with the same overall structure as the lectures.
  • The concepts are rehearsed using exercises in 5 practical sessions. Each session concentrates on one particular aspect of the theory and usage of wavelets.
  • The students make a project, alone or in pairs, in which wavelets are being used to solve a practical problem in image or signal processing (such as compression, fingerprinting, ...)

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Lecture Notes, example material, Toledo

Wavelets with Applications in Signal and Image Processing: Exercises and Laboratory Sessions (B-KUL-H03F8a)

1 ECTS : Practical 12 First termFirst term

Content

  • 6 lectures are used to introduce the theory of wavelets. The focus of these lectures lies on the insights and the how-and-why of wavelets. This complements the course notes, which contain a more complete and in-depth treatment of wavelets, but with the same overall structure as the lectures.
  • The concepts are rehearsed using exercises in 5 practical sessions. Each session concentrates on one particular aspect of the theory and usage of wavelets.
  • The students make a project, alone or in pairs, in which wavelets are being used to solve a practical problem in image or signal processing (such as compression, fingerprinting, ...)

Wavelets with Applications in Signal and Image Processing: Project (B-KUL-H06P7a)

2 ECTS : Assignment 0 First termFirst term

Content

  • 6 lectures are used to introduce the theory of wavelets. The focus of these lectures lies on the insights and the how-and-why of wavelets. This complements the course notes, which contain a more complete and in-depth treatment of wavelets, but with the same overall structure as the lectures.
  • The concepts are rehearsed using exercises in 5 practical sessions. Each session concentrates on one particular aspect of the theory and usage of wavelets.
  • The students make a project, alone or in pairs, in which wavelets are being used to solve a practical problem in image or signal processing (such as compression, fingerprinting, ...)

Evaluatieactiviteiten

Evaluation: Wavelets with Applications in Signal and Image Processing (B-KUL-H23F7a)

Type : Exam during the examination period
Description of evaluation : Oral
Type of questions : Open questions

Explanation

Prior to the exam, the student reads two papers that have appeared in scientific literature and that are related to wavelets or the use of wavelets in applications. The exam consists of an oral discussion of these papers with the examinator. A suggested list of suitable papers is provided. The student can also propose a different paper in his or her area of interest which is not in this list, or in the list of previous years. In this case, the student should seek explicit approval of the paper from the examinator for use in the exam.

The grades for the course are based on the exam and on the project assignment.

ECTS Parallel Computing (B-KUL-H03F9A)

4 ECTS English 33 First termFirst term Cannot be taken as part of an examination contract
Meerbergen Karl (coordinator) |  Diehl Martin |  Meerbergen Karl

Aims

The aim of the course is to provide insight into the key issues of parallel high performance computing and into the design and performance analysis of parallel algorithms.
The students should be able to design and analyse parallel algorithms with simple data dependencies, both in the shared memory programming model, available on multicore systems, as well as in the distributed memory programming model, available on HPC clusters.

Previous knowledge

Skills: the student must be able to analyze, synthesize and interpret scientific texts and results at master program level.
Knowledge: programming in Java or C/C++, algorithms for basic numerical and non-numerical tasks (matrix operations, sorting, ...).

Onderwijsleeractiviteiten

Parallel Computing: Lecture (B-KUL-H03F9a)

3 ECTS : Lecture 20 First termFirst term

Content

This course deals with the design, implementation and performance analysis of parallel algorithms. First, the architecture of parallel computers (multicore systems, HPC clusters) is briefly reviewed. Several programming models (shared address space, message passing, ...) are described. The main part of the course deals with parallel algorithms for a number of model problems, including matrix operations, sorting, operations on graphs. Some papers on more advanced topics (e.g. load balancing) are studied.

  • Standard concepts of parallel algorithms: speed-up, law of Amdahl, scalability, pipelining, classification (SISD, SIMD, MIMD), levels of parallelism
  • Organisation of computer hardware: memory hierarchy, multicore machine, arithmetic intensity, temporal and spatial locality, interconnects, programming models
  • Distributed memory and message passing: point to point communication, collective operations, MPI, communication hiding and avoidance
  • Shared memory and multithreading: threads, OpenMP
  • Parallel matrix vector product: partitioning, complexity for dense, tridiagonal, banded and sparse matrices
  • Sorting: bubblesort and quicksort
  • Communication avoidance, commication hiding
  • Other topics: MapReduce, BDMPI

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

- Textbook

- extra material (slides, papers) made available on Toledo

Parallel Computing: Exercises and Laboratory Sessions (B-KUL-H03G0a)

1 ECTS : Practical 13 First termFirst term

Content

Exercises and practical sessions related to the lectures Algorithms for parallel computers.

Course material

- Textbook

- extra material made available on Toledo 

Format: more information

2 or 3 sessions are exercise sessions without access to computers; 2 or 3 sessions are hands-on sessions with access to a multicore system and to a HPC cluster.  The latter sessions are obligatory.

Evaluatieactiviteiten

Evaluation: Parallel Computing (B-KUL-H23F9a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Paper/Project, Report
Type of questions : Open questions
Learning material : Course material

Explanation

The evaluation consists of the written exam in January, the results of an assignment during the exercise sessions and a summary of a scientific paper.

ECTS Numerical Linear Algebra (B-KUL-H03G1A)

6 ECTS English 61 First termFirst term Cannot be taken as part of an examination contract
Vandebril Raf (coordinator) |  Meerbergen Karl |  Rinelli Michele (substitute) |  Vandebril Raf |  Rinelli Michele (substitute) |  Vannieuwenhoven Nick  |  Less More

Aims

Besides the analytical and experimental approach to solve scientific and/or engineering problems, numerical simulation, has become very popular in the last decade. In simulation methods, linear algebra components are quite often the most time and memory consuming parts. In the analysis of large amounts of data and large networks, linear algebra is also playing an increasingly important role, e.g., PCA analysis, and PageRank. The aim of this course is to give the student insight and knowledge related to advanced solution techniques from numerical linear algebra, enabling him or her to make a well-founded decision when selecting the best suited method, taking into account accuracy, reliability and efficiency. The student gained practical experience by implementing and testing some of these algorithms. Moreover, the student is confronted with contemporary research questions within numerical linear algebra by digesting and understanding recent well-chosen research articles.

Previous knowledge

Skills: The student must be able to analyse, synthesize and interpret, and should understand numerical algorithms. Also basic implementation skills are compulsory.

Knowledge: Introductory course(s) on Numerical Methods and Numerical Linear Algebra on Bachelor level.

Onderwijsleeractiviteiten

Numerical Linear Algebra: Lecture (B-KUL-H03G1a)

3 ECTS : Lecture 36 First termFirst term
Meerbergen Karl |  Rinelli Michele (substitute) |  Vandebril Raf |  Rinelli Michele (substitute) |  Vannieuwenhoven Nick

Content

Each year the content of the course is adapted taking into consideration the interests of the students. Frequently recurring subjects are:

  • Sparse matrices
  • Direct methods for sparse linear systems
  • Krylov methods and preconditioning for sparse linear systems
  • Domain Decomposition, Multigrid
  • Methods for solving eigenvalue problems
  • Model order reduction of dynamical systems
  • Pseudospectra and applications
  • Regularization methods

Each year 1 or 2 lectures are presented by external experts, e.g. tensor computations, matrix functions, and matrix manifold optimization.

Course material

Lecture notes, chapters from books, articles, transparancies, toledo.

Format: more information

Because the number of students is not large, the lectures are presented in an interactive fashion, and active cooperation of the students is strongly encouraged. Together with the lecturer, students examine and learn the theory by many practical demonstrations, in which the algorithms are tested explicitly and examined in a critical way.

Numerical Linear Algebra: Exercises and Laboratory Sessions (B-KUL-H03G2a)

1 ECTS : Practical 25 First termFirst term

Content

Through exercise and laboratory sessions the students are becoming familiar with the concepts and methods from the lectures. The Matlab programming environment is used. The students experiment with Matlab-code, make changes to it and critically analyse the results. In this way, they built up practical experience in solving different problems from numerical linear algebra.

Course material

The problems for the exercise and laboratory sessions are made available in Toledo.

Numerical Linear Algebra: Project (B-KUL-H09N2a)

2 ECTS : Assignment 0 First termFirst term

Content

Besides the homeworks, the students choose a recent scientific paper depending on their interests. This paper is read and analysed on an individual basis or in a group of two students.

The students give a presentation of this paper while the other students and the complete educational team are listening. After the presentation a critical discussion on the paper and the topic follows. Finally, feedback is given on the content as well as on the way of presenting the material.

Course material

The topics of the homeworks as well as a list with interesting papers is made available through Toledo.

Evaluatieactiviteiten

Evaluation: Numerical Linear Algebra (B-KUL-H23G1a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Report
Type of questions : Open questions
Learning material : Course material, Calculator

Explanation

Partial continuous evaluation through reports on the homeworks.

ECTS Deterministic and Stochastic Integration Techniques (B-KUL-H03G3B)

6 ECTS English 55 Second termSecond term Cannot be taken as part of an examination contract
Cools Ronald (coordinator) |  Cools Ronald |  Samaey Giovanni

Aims

In many applications, engineers and scientists are confronted with integrals, for instance when computing the expected value of a stochastic process (of which the evolution cannot be described deterministically). When these integrals cannot be computed analytically, one needs to construct an approximate numerical solution.  In this course, students acquire a basic knowledge on several deterministic and stochastic (Monte Carlo) integration techniques for the numerical computation of integrals. After this course, the student is able to:

  • describe standard deterministic en stochastic discretisation methods for the numerical approximation of integrals;
  • describe common stochastic processes and the corresponding numerical integration techniques;
  • explain the behaviour of these methods, and discuss their advantages and limitations;
  • show and interprete relations between the different methods;
  • argue which (deterministic or stochastic) methods are suitable for specific types of problems (high-dimensional vs. low-dimensional, specific properties of the integrand, etc.);
  • use the acquired knowledge to select and implement a specific method for a concrete application, and verify the correct operation of the resulting method.

Previous knowledge

A significant basis knowledge of calculus, including differential equations, and experience with numerical techniques.

Onderwijsleeractiviteiten

Deterministic and Stochastic Integration Techniques: Lecture (B-KUL-H03G3a)

4.5 ECTS : Lecture 30 Second termSecond term

Content

Deel 1 : Approximation techniques for integrals.
Approximating integrals is an important step in many numerical models. This course starts with an overview of the classical deterministic approach, after which Monte Carlo methods are introduced. These methods are used in many simulations and are traditionally introduced via methods to approximate multidimensional integrals. Deterministic and stochastic methods are put next to each other to highlight their advantages and limitations. The focus lies on the understanding of basic concepts, the implemenation of integration methods, including adaptivity and error estimation, and the application of the methods.
1. Deterministic approach for the approximation of Riemann integrals. This is not limited to integration rules that are exact for polynomials. Also quasi-Monte Carlo methods are considered, as are techniques for integrals with specific problems, such as strong oscillations and singularities. Practical error estimation and adaptive algorithms are treated, with attention for different types of adaptivity.
2. Monte Carlo methods. Error estimation depends on stochastic error estimators that depend on the variance of the problem. Several techniques for variance reduction are treated, as is the generation of pseudo-random numbers distributed according to a given probability distribution.

Deel 2: Stochastische processen en simulatie
Not all phenomena can be well modelled by deterministic models (ordinary and partial differential equations). When uncertainty is added to such a model, one quickly ends up with a stochastic differential equation. We will see that such problems can be addressed with Monte Carlo methods. In this part, an introduction is given to stochastic integrals and differential equations, along with some numerical solution techniques. All modelling techniques are illustrated with different applications (biology, chemistry, finance, physics). Emphasis lies on the understanding of basic concepts, the implementation of simple numerical techniques, and the application to practical examples.
1. Stochastic differential equations. Concepts, applications and numerical simulation. (Relation between stochastic differential equations and an advection diffusion equation for the corresponding probability density.) First introduction of Monte Carlo simulation.
2. Markov-Chain Monte Carlo (Metropolis-Hastings). This is one of the most influential algorithms of the 20th century. Monte Carlo method based on the properties of stochastic processes.
3. Alternative representations of stochastic processes: jump processes, Markov chains, and their application in biology, physics and chemistry.
4. Equilibria of stochastic systems. Invariant probability distribution. Applications: determination of material properties. Formulation as an integral computation.

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Articles and notes, provided via Toledo.

Format: more information

When the size of the group allows, the lectures will be organised in an alternative way.

  • Before every lecture, two students are assigned (in mutual agreement) to prepare the next lecture thoroughly. The other students prepare the lecture less thoroughly.
  • During the lecture, the assigned students discuss the material, and explain it to their fellow students, who can then ask questions.
  • The lecturer moderates the discussion and guides where needed.

Deterministic and Stochastic Integration Techniques: Exercises and Practica (B-KUL-H03G4a)

1.5 ECTS : Practical 25 Second termSecond term

Content

Exercises and tasks related to the lectures on Deterministic and Stochastic Integration Techniques.

Evaluatieactiviteiten

Evaluation: Deterministic and Stochastic Integration Techniques (B-KUL-H23G3b)

Type : Exam during the examination period
Description of evaluation : Oral
Type of questions : Open questions
Learning material : Course material

ECTS Advanced Methods in Cryptography (B-KUL-H03G5A)

4 ECTS English 40 Second termSecond term Cannot be taken as part of an examination contract

Aims

This course gives a thorough introduction to more advanced topics in modern cryptography, encompassing proper security models, cryptanalysis, and implementations attacks. The course deals with analytical methods and concepts in modern cryptography and how these can influence not only the design, but also the use and implementation of cryptosystems.  The selected topics are as follows:

·             Cryptanalytic algorithms determine largely the design and parameter choice of cryptographic algorithms

·             Provable security introduces the different modern definitions of encryption security / signature algorithm and shows why such strong definitions are necessary

·             Implementation attacks make use of information that leaks during the execution of an algorithm, such as time or power usage, to reconstruct the secret key. These attacks influence especially the way the cryptographic algorithms should be implemented.

·             FHE (Fully Homomorphic Encryption) are two recent technologies that allow us to compute on encrypted data in a privacy preserving manner, i.e. without leaking any information about the underlying data.

·             Post-quantum cryptography exemplifies the threat posed by quantum computers and how these can be mitigated by novel cryptographic schemes.

Following this course enables the student to make a first security analysis of a new cryptographic algorithm and to determine to which modern security definitions it conforms. This course bridges abstract discrete mathematics and these security analyses, and will therefore be taught from both points of view.

Previous knowledge

Skills: the student should be able to analyze, synthesize and interpret
Knowledge: basic knowledge of algebra (e.g. H01G5A), knowledge of cryptography and network security (e.g. H05D9A/H05E1A) is useful, but not necessary.

Onderwijsleeractiviteiten

Advanced Methods in Cryptography: Lecture (B-KUL-H03G5a)

2 ECTS : Lecture 20 Second termSecond term

Content

The course consists of lectures covering the following topics:

·         Symmetric key cryptanalysis: Differential cryptanalysis,  linear cryptanalysis, design of block ciphers

·         Public key constructions: Elliptic curve integrated encryption scheme, digital signature algorithm, full domain hash

·         Symmetric key constructions: Modes of operation, MAC functions, AEAD constructions.

·         Provable security & security models for encryption and signatures: perfect, semantic, polynomial security, passive, chosen ciphertext, adaptive chosen ciphertext attack; selective, existential forgeries, Fiat-Shamir transformation

·         Fully homomorphic encryption: homomorphic encryption, applications and limitations, integer-based encryption scheme, Gentry’s construction, bootstrapping

·         Post-quantum cryptography: mechanics of quantum computers, Shor’s algorithm, Learning With Errors problem, Regev encryption

·         Side channel attacks & countermeasures: Timing, power and EM radiation, SPA and DPA attacks, Simple countermeasures, Fault attacks, RSA and Chinese remaindering, partial key attacks

 


 

Course material

The course text consists of slides, overview articles and scientific articles. Additional information is provided on Toledo. 

Advanced Methods in Cryptography: Exercises and Laboratory Sessions (B-KUL-H03G6a)

2 ECTS : Practical 20 Second termSecond term
N. |  Delpech de Saint Guilhem Cyprien (substitute)

Content

See lectures.

Course material

Problem sheets will be provided beforehand so that the students can familiarize themselves with the exercises. 

Format: more information

Solving exercises.

Evaluatieactiviteiten

Evaluation: Advanced Methods in Cryptography (B-KUL-H23G5a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions
Learning material : Course material, Calculator

Explanation

The exam during the examination period is a written exam. It is an open book exam; additional texts may be consulted.

The exam consists of exercises and/or strength-weakness analysis and/or comparison of the cryptosystems explained in the course.

 

Information about retaking exams

Assessment is similar to the 1st exam opportunity.

ECTS Industriële stage: Wiskundige ingenieurstechnieken / Industrial Internship: Mathematical Engineering (B-KUL-H03G7A)

6 studiepunten Nederlands 120 Eerste semesterEerste semester Uitgesloten voor examencontract Uitgesloten voor creditcontract
Meerbergen Karl (coördinator) |  De Moor Bart |  Meerbergen Karl

Doelstellingen

Het opleidingsonderdeel stage is bedoeld als kennismaking met de werkomgeving van een bedrijf en het opdoen van industriële ervaring. Het is de bedoeling een klein industrieel project volledig uit te werken, dat aanleunt bij de opleiding in een reële bedrijfsomgeving.

Begintermen

Afhankelijk van het gekozen bedrijf.
 
Beginvoorwaarden: Alle verplichte opleidingsonderdelen van het eerste masterjaar.

Onderwijsleeractiviteiten

Industriële stage: Wiskundige ingenieurstechnieken / Industrial Internship: Mathematical Engineering (B-KUL-H03G7a)

6 studiepunten : Stage 120 Eerste semesterEerste semester

Inhoud

De stage omvat een werkverblijf met een minimale duur van 6 weken in een bedrijf. Gedurende deze periode zal de student activiteiten uitoefenen die voldoen aan de hoger gestelde doelstellingen. Hij zal hierover achteraf schriftelijk en mondeling rapporteren.

Evaluatieactiviteiten

Evaluatie: Industriële stage: Wiskundige ingenieurstechnieken / Industrial Internship: Mathematical Engineering (B-KUL-H23G7a)

Type : Permanente evaluatie zonder examen tijdens de examenperiode
Evaluatievorm : Verslag, Presentatie
Vraagvormen : Open vragen
Leermateriaal : Geen

Toelichting

De evaluatie gebeurt op basis van de volgende delen:

  • Verslag (15-25 blz.): na de stage maakt de student (in overleg met het bedrijf) onmiddellijk een rapport op. Dit wordt aan de stagecoördinator bezorgd.
  • Logboek: de stagair maakt dagelijks een korte beschrijving van de activiteiten van de dag
  • Presentatie: de student stelt mondeling kort zijn stage voor op een overeengekomen tijdstip.
  • Evaluatieformulier ingevuld door het bedrijf

Het stagerapport bestaat uit zes delen:

  • Administratieve gegevens: naam van de student, studiejaar, naam en adres van het bedrijf, naam van de contactpersoon in het bedrijf, naam van de stagecoördinator en de stageperiode.
  • De inleiding situeert het bedrijf: het (hoofd)product van het bedrijf, de plaats van het bedrijf in zijn sector.
  • Het hoofdgedeelte start met een korte, precieze beschrijving van de stageopdracht en plaatst dit in het geheel van het bedrijf. Daarna volgt een wat uitgebreidere beschrijving samen met de eventuele resultaten. Tracht steeds aan te geven wat uw taak precies geweest is.
  • Het derde deel geeft de belangrijkste besluiten weer: de behaalde resultaten, datgene wat de stage heeft bijgebracht (oa. competenties),...
  • Daarna een overzicht van het verband tussen de stage en reeds gevolgde opleidingsonderdelen.
  • Het logboek dat de werkzaamheden dag per dag kort beschrijft.

Toelichting bij herkansen

De evaluatie betreft de mogelijkheid tot een betere rapportering (verslag en presentatie). De stage zelf kan niet opnieuw gedaan worden.

ECTS Medical Imaging and Analysis (B-KUL-H03H5A)

6 ECTS English 56 Second termSecond term

Aims

After succesful completion of this course, the student should understand and be able to explain the physical and mathematical principles of medical imaging and image analysis. The student should have knowledge of and insight in the image data acquisition process of the main imaging modalities (RX, CT, MRI, SPECT/PET, US), the image reconstruction methods, the parameters that influence image quality (resolution, contrast, noise, artefacts), biological safety aspects and the processing and visualization of medical images. The main focus of the course is on the methodological concepts of various imaging and image analysis techniques, while imaging equipment and clinical applications are treated in less detail. 

After succesful completion of the course, the student should be able to relate the various physical principles underlying different imaging modalities to the complementary information different medical images provide for diagnosis and therapy planning. The student should also be able to appreciate the intrinsic connection between imaging and mathematics and the engineering challenges to bring these concepts into practice. 

Previous knowledge

Preliminary terms
A basic education in engineering, physics or mathematics is required.
The student must understand and command the basic concepts of digital signals and linear system theory, in particular Fourier theory.


Preliminary conditions
Having obtained credits in a course on linear system theory.

Is included in these courses of study

Onderwijsleeractiviteiten

Medical Imaging and Analysis: Lecture (B-KUL-H03H5a)

4.83 ECTS : Lecture 36 Second termSecond term

Content

The course follows the textbook 'Fundamentals of Medical Imaging" by Prof. em. Paul Suetens. 

In Chapter 1, an introduction to digital image processing is given. It introduces the terminology used, the aspects defining image quality, and basic image operations to process digital images.

Chapters 2 - 6 explain how medical images are obtained. The most important imaging modalities today are discussed: radiography (Chapter 2), computed tomography (Chapter 3), magnetic resonance imaging (Chapter 4), nuclear medicine imaging (Chapter 5), and ultrasonic imaging (Chapter 6). Each chapter includes (1) a short history of the imaging modality, (2) the theory about the physics of the signals and their interaction with tissue, (3) the image formation or reconstruction process, (4) a discussion of the image quality, (5) the different types of equipment today, (6) examples of the clinical use of the modality, (7) a brief description of the biologic effects and safety issues, and (8) some future expectations.

Chapters 7 gives an overview of medical image analysis approaches to extract quantitative information from the images to support diagnosis and therapy planning and presents some model-based strategies to deal with ambiguity in the images.

Chapter 8 describes 3D visualisation approaches and their use for image-based guidance during treatment and surgical interventions. 

 

Course material

Study cost: More than 100 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Textbook: P. Suetens, Fundamentals of Medical Imaging, 3rd edition, Cambridge University Press, 2017.

Course material available on Toledo:

- PDF version of each chapter of the textbook for personal use only

- Slides and handouts per chapter

- Course notes with additional explanations

- Exercises and solutions

- A list of equations

- An appendix with basic notions of linear system theory

 

Format: more information

There are +/- 18 lectures of 2h each. The scheme of the lectures is planned as follows:

Lecture 1: Course organization. 

Lecture 1-2: Basics of digital image processing

Lecture 2-3: RX

Lecture 4-5: CT

Lecture 6-7-8-9: MRI

Lecture 10-11: SPECT/PET

Lecture 12-13: US

Lecture 14-15-16: Image analysis

Lecture 17: 3D visualization

The remaining lecture is used as back-up in case a lecture is cancelled.

Medical Imaging and Analysis: Exercises and Laboratory Sessions (B-KUL-H03H6a)

1.17 ECTS : Practical 20 Second termSecond term

Content

The exercise sessions are intended to foster insight by making the various concepts from the lectures more tangible with numerical examples and by exploring the underlying assumptions, benefits and limitations of specific imaging setups. The exercise sessions are organized in line with the course chapters. A guided tour in the university hospital is also organised as part of the exercise sessions. 

Session 1: Basic image processing.

Session 2-7: Imaging modalities: RX, CT, MRI, US, SPECT/PET

Session 8: Image analysis & Visualization for diagnosis and therapy

Session 9: Guided tour within the departments of Radiology and Nuclear Medicine of UZ Gasthuisberg, Leuven.

 

Course material

A list of exercises per chapter and their solutions are provided on Toledo.

Evaluatieactiviteiten

Evaluation: Medical Imaging and Analysis (B-KUL-H23H5a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions
Learning material : List of formulas

ECTS Biomedical Data Processing (B-KUL-H03I2A)

6 ECTS English 60 First termFirst term

Aims

·       Being able to understand the basic techniques in biomedical signal processing, analysis and pattern recognition. These include understanding of physiological signals and their processing challenges; preprocessing (filtering and artefact removal), event detection and (linear) feature definition, signal transforms (Fourier, wavelets, etc.), waveform analysis, parametric and nonparametric signal representation, blind source separation; classification and decision support

·       being able to apply these methods to solving real-life biomedical data processing problems using Matlab code implementations;

·       being able to interpret, analyse, and critically compare the potential and limitations of various approaches for the biomedical problem at hand;

·       at the end, being able to design a solution for a biomedical data processing problem, starting from the raw data after acquisition until the diagnostic level, and work out the required data processing steps in Matlab code.

Previous knowledge

Preliminary terms

A good background in signals and systems and digital (discrete-time) signal processing is required. Students should already be familiar with different model representations and transformations of signals and systems, such as difference equations, pole-zero diagrams, z-transform, Laplace and Fourier transforms, discrete-time Fourier transform (DTFT) and Discrete Fourier Transform (DFT). We expect basic knowledge of how to program in Matlab (a written tutorial on Matlab will be made available, but the exercise sessions will assume knowledge of Matlab from the start).

 

Preliminary conditions

The student must have obtained credits for BOTH of the following courses (or equivalent courses):

1) A course on Systems Theory or Signals & Systems (for example B-KUL-H08U4A or B-KUL-H01M8A) 

2) A basic course on digital signal processing (for example B-KUL-H01L6A or B-KUL-T34DPE).

Identical courses

H06Z6A: Dataverwerking voor de gezonde mens
I0T95A: Human Health Data Processing

Is included in these courses of study

Onderwijsleeractiviteiten

Biomedical Data Processing: Lecture (B-KUL-H03I2a)

3 ECTS : Lecture 30 First termFirst term

Content

The course consists of various parts; each of which deals with a certain type of problem in biomedical signal processing, analysis or modelling, including the principles of pattern recognition. At the start, various biomedical signals are introduced, and processing challenges are illustrated for which later algorithms can be designed. Next, a variety of signal processing, modelling or analysing techniques will be discussed: starting from relatively simple methods, followed by more advanced approaches specific to the issue at hand. 

Covered topics: 

- an illustrated introduction to common biomedical signals (EEG, ECG, EMG, PPG, speech, …) and relevant applications.
- filtering for deleting artefacts, an important pre-processing step 
- techniques that are useful for event detection in biomedical signals, waveform analysis and waveform complexity analysis
- frequency domain techniques for the characterisation of biomedical signals and systems.
- modelling of biomedical signals and systems, enabling a parametrical representation and analysis.
- analysis of non-stationary signals.
- biomedical pattern recognition and diagnostic decisions. An introductory overview will be given on linear and non-linear pattern recognition techniques.
- signal processing on multiple channels: Principal Component Analysis for dimensionality reduction and Independent Component Analysis to split the multichannel signal into separate source signals (such as muscle artefacts, ECG, breathing…)

- wavelet analysis (transformations, decomposition) to decompose the signal into frequency components with different time-frequency resolutions.

Course material

Study cost: 76-100 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Course material consists of an introductory handbook (which covers most topics and can be purchased in ACCO), powerpoint slides of each lecture (available on Toledo), and online material on Toledo for self-study and further illustrations.
Book: Rangaraj M. Rangayyan, BIOMEDICAL SIGNAL ANALYSIS: A Case-Study Approach'', John Wiley & Sons, Inc., New York, 2002.
 

Format: more information

Lecture.

Biomedical Data Processing: Exercises (B-KUL-H03I3a)

3 ECTS : Practical 30 First termFirst term

Content

This teaching activity consists of 6(+1) exercise sessions. In the first session the student learns how to acquire electrophysiological signals such as ECG and EMG. The other 5 sessions consist of solving problems on the computer and implementing the methods taught during the lectures. Attendance of these sessions is obligatory. The first 4 computer sessions are educational: the marks do not count for the end evaluation. The assignments also prepare for the ‘interpretation questions’ on the exam. The last session is part of the evaluation and introduces a project for which the code has to be uploaded to Toledo. A final session will be scheduled during which each team’s solution is evaluated ‘on screen’. During this on-screen evaluation, the team will be challenged with questions from the didactical team (see ‘evaluation’).

 

Evaluatieactiviteiten

Evaluation: Biomedical Data Processing (B-KUL-H23I2a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Paper/Project
Type of questions : Open questions
Learning material : Course material, Computer, Reference work

Explanation

The evaluation consists of a computer project in Matlab and a final exam:

1) Computer project: 
The computer project can be made in groups of two students. An extra computer exercise session is part of the course evaluation and is evaluated on computer screen in a separate session during the year. For that exercise, the team uploads their carefully documented code via Toledo before the agreed deadline (will be communicated on Toledo). The code should generate figure results for the last computer exercise, with which the students prove to be able to understand, implement, and apply the taught methods correctly, and to interpret the results.  Software plagiarism is forbidden: if detected, the student is referred to the faculty examination commission and punished. See the course instructions on Toledo to understand what is treated as software plagiarism (this also includes copying, manipulating, or modifying code from fellow students or from online sources without mentioning the source). Hiding software plagiarism by manipulating/reworking existing code is treated as fraud.

2) Final evaluation during the examination session:
The exam is based on answers on theoretical questions +  some interpretation questions testing biomedical signal processing insights on specific problems. This is a written examination.

The computer exercises are obligatory and evaluation of the graded computer session counts for 35%, theory and interpretation questions for 65% of the final score. The code must have been submitted before the deadline, otherwise no marks are given for that part of the evaluation.

 

If, for reasons of force majeure, the faculty decides that the preparation time for an oral exam must be limited to less than an hour, or that an oral exam can not take place on campus, it is possible that the exam will be replaced by a written exam. The impact of this decision will be explained on Toledo. If, for reasons of force majeure, the faculty decides that the laboratory sessions cannot go ahead in their current form, they will take place online. In this case, there is still the obligation to hand in a report  by the original deadline.

Information about retaking exams

When retaking the exam of this course, the following instructions apply:

A written report for the computer exercises is additionally requested, demonstrating understanding of the obtained figures / results. The (updated) code and a detailed report needs to be submitted before the deadline on Toledo. This report will be evaluated for 35% of the mark.

The rest of the evaluation is equivalent to the initial exam.

ECTS Data Mining and Neural Networks (B-KUL-H03V7B)

4 ECTS English 32 First termFirst term

Aims

Content:

Many application areas are characterized by a growing number of data, which are available and should be explored for improved modelling, efficient and automatic processing of data and extracting knowledge from the data. Typical examples include pattern recognition, biomedical engineering and bioinformatics, signal processing and system identification, process industry, fraud detection, web mining, e-commerce, financial applications, etc. In each of these areas, artificial neural networks are an important technique for analysis and design of systems. Neural networks are universal approximators, possess a parallel architecture and learn on-line or in batch mode from given sample patterns and lead to powerful methods for modeling. Training of neural networks can be done either supervised or unsupervised.

This course provides an overview of the main classical and advanced modern techniques on data mining and neural networks. Commonly used types of neural networks (such as multilayer perceptrons, radial basis function networks) are discussed, including structure, learning algorithms, optimization methods, on-line versus batch training, generalization aspects, validation, feedforward and recurrent networks, statistical interpretations, pruning, variance reduction, decision functions, density estimation and regularization theory. Special attention is given to efficient and reliable algorithms for classification and function estimation and processing of large data sets for data mining applications. Furthermore, emphasis is given on preprocessing, feature selection, dimensionality reduction and incorporation of expert knowledge. In addition to the classical neural network techniques in supervised learning more advanced methods are also addressed such as Bayesian inference, deep learning, statistical learning theory and support vector machines. With respect to unsupervised learning, cluster algorithms (and related methods such as EM algorithm), vector-quantization and self-organizing maps are discussed. Starting from linear and nonlinear principal component analysis, principles of stacked autoencoders and convolutional neural networks are explained for deep learning. Furthermore, deep learning based on attention and transformers, and generative models are discussed.

Lectures:

1. Introduction
2. Multilayer feedforward networks and backpropagation
3. Nonlinear modelling and time-series prediction
4. Classification and Bayesian decision theory
5. Generalization, Bayesian learning of neural networks
6. Vector quantization, self-organizing maps, regularization theory
7. Basic principles of support vector machines and kernel-based models
8. Nonlinear principal component analysis, autoencoders, deep learning with stacked autoencoders and convolutional neural networks
9. Generative models: deep Boltzmann machines, generative adversarial networks, variational autoencoders, others
10. Normalization, attention, transformers

 

Previous knowledge

basic knowledge of linear algebra

Is included in these courses of study

Onderwijsleeractiviteiten

Data Mining and Neural Networks: Lectures, Part 1 (B-KUL-H05R4a)

2.5 ECTS : Lecture 16 First termFirst term

Content

Lectures:

1. Introduction
2. Multilayer feedforward networks and backpropagation
3. Nonlinear modelling and time-series prediction
4. Classification and Bayesian decision theory
5. Generalization, Bayesian learning of neural networks
6. Vector quantization, self-organizing maps, regularization theory
7. Basic principles of support vector machines and kernel-based models
8. Nonlinear principal component analysis, autoencoders, deep learning with stacked autoencoders and convolutional neural networks
9. Generative models: deep Boltzmann machines, generative adversarial networks, variational autoencoders, others
10. Normalization, attention, transformers

Course material

- English course text in toledo

- Slides of the lectures are available in toledo

Format: more information

- Lectures and computer exercise sessions

- Report of the exercise sessions

Is also included in other courses

G9X29A : Data Mining and Neural Networks

Data Mining and Neural Networks: Lectures, Part 2 (B-KUL-H05R5a)

0.6 ECTS : Lecture 6 First termFirst term

Is also included in other courses

H03V7C : Advanced Data Mining and Neural Networks

Data Mining and Neural Networks: Training Sessions, Part 1 (B-KUL-H05R6a)

0.5 ECTS : Practical 6 First termFirst term

Content

computer exercise sessions

Format: more information

Report of the exercise sessions

Is also included in other courses

G9X29A : Data Mining and Neural Networks

Data Mining and Neural Networks: Training Sessions, Part 2 (B-KUL-H05R7a)

0.4 ECTS : Practical 4 First termFirst term

Is also included in other courses

H03V7C : Advanced Data Mining and Neural Networks

Evaluatieactiviteiten

Evaluation: Data Mining and Neural Networks (B-KUL-H23V7b)

Type : Exam during the examination period
Description of evaluation : Oral, Written
Type of questions : Open questions
Learning material : Course material

ECTS Engels in de bedrijfsomgeving (B-KUL-H04B3A)

3 studiepunten Engels 39 Beide semestersBeide semesters Uitgesloten voor examencontract

Doelstellingen

Deze cursus heeft als doel om studenten voor te bereiden om op een adequate en professionele manier te communiceren in een Engelstalige bedrijfsomgeving. De cursus focust op een aantal belangrijke communicatieve vaardigheden (zowel schriftelijk als mondeling), maar heeft ook aandacht voor het professionaliseren van woordenschat en grammatica. De cursus wordt verzorgd door mevr. Annelien De Geest.

Begintermen

De voorkennis die van de studenten wordt verwacht, is die van het vak Engels op het einde van het secundair onderwijs (niveau B1 van het Europees Referentiekader).
Concrete informatie : zie taalopleidingsonderdelen

  • Aan het begin van het academiejaar nemen de studenten deel aan een verplichte diagnostische taaltest. De studenten dienen zich hiervoor in te schrijven via de tool op bovenstaande webpagina. Alle praktische informatie over de test (datum / plaats) kan ook daar gevonden worden.
  • Studenten wonen de lessen 1 keer per week bij, gedurende beide semesters. Studenten geven bij registratie aan wanneer ze beschikbaar zijn en krijgen bevestiging van hun keuzes na de test.
  • Het resultaat van de test wordt opgevat als didactische informatie zowel voor de student als voor de taaldocent. In het uitzonderlijke geval dat er te veel vraag is voor de taalcursus of het cursusniveau ongeschikt is, heeft het testresultaat een beslissende impact.
  • Voor studenten die toelating hebben om twee talen te volgen: de diagnostische test is verplicht voor Engels H04B3A en Frans H04B4A.

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Engels in de bedrijfsomgeving (B-KUL-H04B3a)

3 studiepunten : College 39 Beide semestersBeide semesters

Inhoud

De cursus bestaat uit een aantal modules:

  • Professionele schriftelijke communicatie, o.a. e-mails, rapporten en sollicitaties
  • Beschrijven van grafieken, duiden van trends en processen
  • Presentatietechnieken
  • Vergaderingen in het Engels leiden en bijwonen
  • Opfrissen en remediëren van grammatica
  • Uitbreiden van professionele woordenschat (business vocabulary)

Studiemateriaal

Verplicht studiemateriaal:

  • Business Vocabulary in Use - Advanced (3rd edition, 2017) door Bill Mascull (Cambridge University Press)
  • Course Notes: English for Professional Purposes door Annelien De Geest (syllabus)

Toelichting werkvorm

Interactieve communicatieve aanpak die leidt tot dieper taalinzicht. Dit veronderstelt dat de studenten thuis een aantal activiteiten voorbereiden zodat de contacturen grotendeels aan de mondelinge en schriftelijke communicatie kunnen worden besteed.

Evaluatieactiviteiten

Evaluatie: Engels in de bedrijfsomgeving (B-KUL-H24B3a)

Type : Permanente evaluatie zonder examen tijdens de examenperiode

Toelichting

Permanente evaluatie (mondeling en schriftelijk) van de activiteiten in de loop van het jaar. Het gaat voornamelijk om vaardigheidsevaluatie en gedeeltelijk ook om kennisevaluatie (woordenschat, grammatica, basisprincipes).

Het resultaat wordt berekend en uitgedrukt met een geheel getal op 20. Het examenresultaat is een gewogen cijfer dat als volgt wordt bepaald:

Woordenschat en grammaticatest (25%) - tijdens de lesuren 

Schriftelijke vaardigheden (30%)

  • Rapport (proposal): 15%
  • CV en sollicitatiebrief: 15%

Mondelinge vaardigheden (45%)

  • Zakelijke meetings (incl. schriftelijke component): 35%
  • Presentatievaardigheden: 10%

Indien de student niet deelneemt aan één (of meerdere) van de deelevaluaties op het vooraf vastgestelde tijdstip dat gepubliceerd wordt op Toledo, wordt de beoordeling van de niet afgelegde deelevaluatie(s) meegeteld als een 0-score binnen het gewogen eindresultaat. De student kan enkel uitstel krijgen als hij omwille van ziekte/overmacht afwezig is en een doktersattest kan voorleggen.

Toelichting bij herkansen

De evaluatiekenmerken en bepaling eindresultaat van de tweede examenkans zijn niet identiek aan die van de eerste examenkans. Wegens de aard van de mondelinge taken (zie toelichting eerste examenkans) worden de behaalde resultaten voor het onderdeel 'zakelijke meetings'  bij de eerste examenkans overgedragen naar de tweede examenkans. Deze component kan dus niet opnieuw afgelegd worden en telt mee voor 35% van het examenresultaat.

Woordenschat en grammatica test (25%)  

Schriftelijke vaardigheden (30%)

  • Rapport (proposal): 15%
  • CV en sollicitatiebrief: 15%

Mondelinge vaardigheden (45%)

  • Zakelijke meetings (incl. schriftelijke component): 35%  => voor deze component worden de behaalde resultaten van de eerste examenkans overgedragen naar de tweede examenkans
  • Presentatievaardigheden: 10%

ECTS Industrial Automation and Control (B-KUL-H04D0A)

6 ECTS English 56 First termFirst term

Aims

Students understand systems used for industrial control and automation.

They can design and evaluate feedback control systems, in the time domain (via root loci), in the frequency domain, and in the state space.

They can explain and apply the concepts related to dependability; the know the basic fault tolerance architectures and can make a motivated choice for a particular application, with specific focus on functional safety and information security.

The students have mastered several methods to determine dependability in a quantitative manner (reliability block diagrams, markov chains, analytical methods). 

In addition, they can position trends in research and development  within the domain.

Previous knowledge

Knowledge at bachelor level of system theory and control theory, including the mathematical techniques.

Basic knowledge of a program such as Matlab.

Is included in these courses of study

Onderwijsleeractiviteiten

Industrial Automation and Control: Lecture (B-KUL-H04D0a)

4.82 ECTS : Lecture 36 First termFirst term

Content

Part 1 feedback control of dynamic systems

  • Dynamic models, response and feedback
  • Root locus design method 
  • Frequency-response design method
  • State space design
  • Digital control
  • Nonlinear systems
  • Control system design: principles and applications

 Part 2: dependable control and automation 

  • Dependability: concepts & terminology 
  • Architectures for dependable systems 
  • Quantitative assessment of dependability 
  • Functional safety of digital systems 
  • Information security 
  • Case studies

Course material

Study cost: 76-100 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

G.F.Franklin, J.D.Powell, A.Emami-Naeini, "Feedback Control of Dynamic Systems (8th ed.),"  Upper Saddle River, NJ: Pearson Education, 2020, ISBN: 9781292274522

Course material available at  VTK (hand-outs & texts)

Industrial Automation and Control: Exercises and Laboratory Sessions (B-KUL-H04D1a)

1.18 ECTS : Practical 20 First termFirst term

Content

4 sessions about control

  • control system design in the frequency domain
  • state-space design
  • discrete (digital) design 
  • non-linear systems

 
4 sessions around robust automation

  • (double session) debugging an industrial control system
  • reliability block diagrams
  • Markov analysis for reliability and others

Course material

Exercises;  hands-on session in a university college

Evaluatieactiviteiten

Evaluation: Industrial Automation and Control (B-KUL-H24D0a)

Type : Exam during the examination period
Description of evaluation : Oral
Type of questions : Open questions
Learning material : Course material, Calculator

Explanation

Oral exam, with questions on the theory, and exercises.

 

Information about retaking exams

Retake exams follow the same procedure as the inital ones.

ECTS Capita selecta ingenieurswetenschappen II.1. (Athens / Summer Course) (B-KUL-H04K9A)

3 studiepunten Nederlands 30 Eerste semesterEerste semester Uitgesloten voor examencontract
Smets Ilse (coördinator) |  N.

Doelstellingen

Inzicht verschaffen in een onderwerp binnen de ingenieurswetenschappen door middel van deelname aan een internationale uitwisseling (ATHENS) of een op voorhand door de programmadirecteur goedgekeurde ‘summer course’. Voor dit opleidingsonderdeel volgt de student een opleidingsonderdeel in het buitenland in het kader van het ATHENS-uitwisselingsprogramma  of een ‘summer course’, mits de programmadirecteur hiervoor op voorhand zijn akkoord heeft gegeven.

De student mag maximaal 1 keer per academiejaar en 2 keer tijdens de masteropleiding deelnemen aan een ATHENS-week. Deelname aan ATHENS is enkel mogelijk na applicatie via de faculteit en selectie door het ATHENS-netwerk. Meer informatie.

 

Begintermen

De kennis en attitudes zoals aangebracht in de bachelor ingenieurswetenschappen.

De student moet voldoen aan de vereiste basiskennis (prerequisites) van het ATHENS-vak dat hij kiest, zoals aangegeven in de course catalogue op de ATHENS inschrijvingswebsite.

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Capita selecta ingenieurswetenschappen II.1. (Athens / Summer Course) (B-KUL-H04K9a)

3 studiepunten : College 30 Eerste semesterEerste semester
N.

Inhoud

Afhankelijk van het opleidingsonderdeel gekozen en toegekend in de buitenlandse instelling na akkoord met de uitwisselingsverantwoordelijke.

Evaluatieactiviteiten

Evaluatie: Capita selecta ingenieurswetenschappen II.1. (Athens / Summer Course) (B-KUL-H24K9a)

Type : Permanente evaluatie zonder examen tijdens de examenperiode
Evaluatievorm : Medewerking tijdens contactmomenten

Toelichting

Dit opleidingsonderdeel wordt geëvalueerd volgens de regels en gebruiken van de gastinstelling waarmee de uitwisseling is gebeurd. De KU Leuven zet deze resultaten om naar PASS/FAIL.
Voor ATHENS-vakken worden, zoals alle andere vakken, in het ISP opgenomen in het academiejaar waarin ze gevolgd zijn.
‘Summer courses’ kunnen enkel gevalideerd worden, indien ze door de uitwisselingsverantwoordelijke voorafgaandelijk goedgekeurd zijn. De student neemt het vak op in het ISP in het academiejaar onmiddellijk volgend op de Summer Course.

Conform het beleid van het ATHENS-netwerk wordt voor ATHENS-cursussen geen 2e examenkans georganiseerd.
 

Toelichting bij herkansen

 

ECTS Numerical Modelling in Mechanical Engineering (B-KUL-H04U3A)

5 ECTS English 52 First termFirst term Cannot be taken as part of an examination contract
Desmet Wim (coordinator) |  Baelmans Tine |  Desmet Wim |  Meyers Johan

Aims

Students are able to explain modern methods for the numerical simulation of phenomena and systems in mechanical engineering. For simple problems, students are able to elaborate a numerical discretization method into a computational code, and verify and validate their implementation. They can use modern commercial software, and can argument good choices of available techniques, and simulation set-up. Finally, students are able to perform a critical assessment of simulation results, based on appropriate mathematical relationships for analysis of numerical methods.

*

The student is able to implement, simulate, and analyze simple 1D and 2D problems from mechanical engineering or energy sciences.
 

*

Starting from different governing equations in the domains of mechanical engineering and energy sciences, the student recognizes the structure and common elements which lead to the formulation of a set of generic numerical discretization schemes. He can argument correct choices for the formulation of partial differential equations and boundary conditions, and is able to implement their numerical discretization for simple model problems. The student is able to describe, explain, and use finite-difference and finite-volume discretization techniques. Moreover is able to perform a critical analysis of discretization methods based on techniques for evaluation of accuracy, stability, and convergence. The student understands, and is able to discuss the potential and limitations of different discretization techniques and methods for numerical analysis.

Previous knowledge

Expected previous knowledge is situated in two different areas:

  • in fundamental knowledge of physical phenomena in mechanics: heat transfer, fluid mechanics, structural mechanics...
  • in basic mathematical techniques: solution of normal and partial differential equations, numeral integration, solution of systems of algebraic equations, eigenvalue problems...

Identical courses

H00R8A: Numerieke modellering in de mechanica

Onderwijsleeractiviteiten

Finite Difference and Finite Volume Modelling: Lectures (B-KUL-H04U4a)

1.72 ECTS : Lecture 13 First termFirst term

Content

In the introduction, and overview is presented of the different type of applications in mechanical engineering and energy sciences focussing on common properties for numerical simulation. The governing partial differential equations are formulated, and complemented with boundary conditions and initial conditions. The finite-difference technique and the finite-volume technique are treated, focussing on

•    Spatial discretization and interpolation 
•    Discrete implementation of boundary conditions
•    Time integration schemes 
•    Linearization of non-linear governing systems
•    Introduction to the use of finite-volume discretization for the numerical simulation of Navier-Stokes equations

In addition, various tools for (numerical) analysis are introduced:

•    Mathematical characterization of partial differential equations and consequences for the selection and placement of boundary conditions
•    Taylor series development for determination of accuracy of numerical schemes, and consequences for convergence properties
•    Analysis of numerical stability using Von Neuman analyses, etc.

Each of these methods and analysis tools is discussed with attention for the necessary conditions for their validity and reliability.
 

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Is also included in other courses

H9X34A : Numerical Methods in Energy Sciences

Finite Difference and Finite Volume Modelling: Seminars (B-KUL-H04U5a)

0.78 ECTS : Practical 13 First termFirst term

Content

See content of the lecture. During the seminars, students program (parts) of numerical discretization and solution procedures in MATLAB.
 

Is also included in other courses

H9X34A : Numerical Methods in Energy Sciences

Finite Elements: Lectures (B-KUL-H04U6a)

1.72 ECTS : Lecture 13 First termFirst term

Content

In the introduction to the course, the different types of applications in mechanics are formulated in a manner that should allow to start simulation. For a wide range of applications, the ruling differential equations are formulated and completed with the boundary conditions and preliminary conditions.
Afterwards, the simulation principles of finite elements will be discussed.
For these methods, the basic principles of model formation and solving techniques are presented. Next to this, attention will also go to the conditions which a model should meet for the results to be considered reliable.
During the lectures, all principles are explained and during the seminars the students make models with commercial software. They also programm parts of solving procedures in MATLAB.

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Is also included in other courses

H0N44A : Numerical Modelling in Biomedical Engineering

Finite Elements: Seminars (B-KUL-H04U7a)

0.78 ECTS : Practical 13 First termFirst term

Content

See the contents of the lecture.
During the lectures, all principles are explained and during the seminars the students make models with commercial software. They also programme part of solving procedures in MATLAB.

Evaluatieactiviteiten

Evaluation: Numerical Modelling in Mechanical Engineering (B-KUL-H24U3a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions
Learning material : Course material

ECTS Numerical Techniques in Fluid Dynamics (B-KUL-H04U8A)

3 ECTS English 20 First termFirst term
Meyers Johan (coordinator) |  Baelmans Tine |  Meyers Johan

Aims

Based on specialised literature, handbooks and own knowledge, the student is able to select an appropriate numerical scheme for simulation problems in fluid mechanics, which appear in application areas related to automobile industry, aerospace, energy conversion technology, air conditioning, etc. The student is capable of implementing these techniques in their most simple version, and is able to verify and validate them.

Previous knowledge

Basic knowledge of numerical methods, and in particular finite-difference, and finite-volume techniques. 
 

Order of Enrolment



SIMULTANEOUS(H04U3A) OR SIMULTANEOUS(H00R8A) OR SIMULTANEOUS(H9X34A) OR SIMULTANEOUS(H00S9A)


H04U3AH04U3A : Numerical Modelling in Mechanical Engineering
H00R8AH00R8A : Numerieke modellering in de mechanica
H9X34AH9X34A : Numerical Methods in Energy Sciences
H00S9AH00S9A : Numerieke methoden in energiewetenschappen

Is included in these courses of study

Onderwijsleeractiviteiten

Numerical Techniques in Fluid Dynamics: Theory Lecture (B-KUL-H04U8a)

1.34 ECTS : Lecture 12 First termFirst term

Content

  •  Formuleren en discretiseren van onsamendrukbare Navier-Stokes (NS) vergelijkingen met behulp van eindige volume techniek.
  • Oplossen van grote niet-lineaire discrete stelsels volgend uit de discretisatie van de NS vergelijkingen: Newtongebaseerde methodes, time marching (expliciet versus impliciet), keuze linearisatie, ontkoppleing van de vergelijkingen.
  • Gebruik van ‘pressure-correction’ technieken
    voor het ontkoppelen van de druk bij linearisatie
  • Inleiding tot het oplossen van grote lineare
    stelsels en gevolgen voor keuzes met betrekking tot linearisatie en
    ontkoppeling
  • Door studenten geselecteerde onderwerpen die naar
    voren gebracht worden in de context van het Projectwerk en de Seminaries




     

Course material

  • J.H. Ferziger, M. Peric, “Computational methods for fluid dynamics”, Springer Verlag, 2002.
  • C. Hirsch, “Numerical computation of internal and external flows”, Vol 1 and 2, Wiley, 1997.

Numerical Techniques in Fluid Dynamics: Project (B-KUL-H04U9a)

1.66 ECTS : Assignment 8 First termFirst term

Content

Implementation of a 2D incompressible Navier-Stokes
solver, starting from a programming skeleton provided in Matlab. Free choice of spatial discretization, linearization, pressure correction method, etc. Verification and validation of the code are performed based on the solution of a Hagen-Poiseuille
flow. Results and discussion of verification and validations are synthesized in the brief report.

The task  is performed in teams of two to three students.
 
Literature study in a specialized topic related to numerical fluid mechanics. At the start of the assignment, two to three relevant papers are provided as a starting basis. Based on the literature survey, a synthesis is presented during a presentation for peer students.
 
The choice of the topic is based on a list:

  • Discretization schemes: flux limiters, numerical diffusion, higher order
  • Discretization errors in LES
  • Energy-conservative discretization
  • Matrix inversion: preconditioning, multi-grid
  • Non-reflecting boundary conditions
  • Automatic grid adaptation
  • Particle-laden flows
  • Free-surface flows
     

Course material

  • Programming skeleton provided in Matlab
  • Two to three papers per selected literature topic are provided by the didactic team.

Evaluatieactiviteiten

Evaluation: Numerical Techniques in Fluid Dynamics (B-KUL-H24U8a)

Type : Continuous assessment without exam during the examination period
Description of evaluation : Paper/Project, Presentation
Type of questions : Open questions
Learning material : Reference work

Explanation

 
The evaluation is based on three aspects:
 

  • The presentation of the selected literature subject to peer students (during the term). 30% of the points
  • The implementation of a NS code (70% of the points), based on the report on the verification and validation, and the submitted code.








     

ECTS Project Management (B-KUL-H04X2A)

3 ECTS English 20 Second termSecond term Cannot be taken as part of an examination contract
Duflou Joost (coordinator) |  Duflou Joost |  Joubert Johan

Aims

The aim of this course is to provide the student with an overview of techniques and means that are available for the start up, execution, follow up and adjustment of large projects. By means of examples and case studies insight is created  supporting recognition of typical patterns, analysis of situations and identification of  suitable methods and/or techniques recommendable for effectively steering projects, with well-optimized chances to reach the  preset project deliverables. 
 

Previous knowledge

This course is not connected to a specific graduation programme. Therefore, the contents of the assignments can be altered to suit the graduation programme of the student. Still, it is recommended to plan this course in a later stage of the master programme to ensure that any lack of technical background will be not be a hindrance in working on specific cases or assignments. Access to a familiar project case (e.g. thesis project) is required in view of the evaluation format which is based on a case study. A possible course on business administration in the curriculum can best be scheduled before attending this course.

Is included in these courses of study

Onderwijsleeractiviteiten

Project Management (B-KUL-H04X2a)

3 ECTS : Lecture 20 Second termSecond term

Content

Introduction

  • What is project management?
  • Situation within the general planning problem
  • Characteristics of projects
  • Project manager
  • Components, concepts and terminology
  • Life cycle of a project: strategical and tactical considerations
  • Factors responsible for the success of a projectOrganisational structures and task allocation
  • Organisational structures
  • Staff management
  • Concurrent engineering
  • Assessment and selection
  • Division of a project
  • Outsourcing or internal work?
  • Conflict evaluation:  within the organisation, environmental effects, othersProject planning
  • Introduction
  • Duration of project activities
  • Learning effects
  • Precedence relations
  • Gantt-representation
  • Arrow network for critical path mathematics
  • Block network for critical path mathematics
  • LP formulation
  • Aggregation of activities
  • Dealing with uncertainty
  • Analysis of PERT and CPM presuppositions
  • Conflicts in planningProject budget
  • Introduction
  • Project budget and company goals
  • Drawing up a budget
  • Budget management
  • FinancingManagement of resources
  • Influence of resource limitations on the project
  • Classification of resources
  • Planning of resources and project with time as a limiting factor
  • Planning of resources and project with resources as a limiting factor
  • Priority rules for the allocation of resources
  • Subcontracting/assessing suppliers
  • Executing projects in parallelProject control
  • Introduction
  • Control systems
  • Following up and controling timewise planning and costs
  • Reporting
  • Updating cost and planning parameters
  • Technological controlComputer support for project management
  • Introduction
  • Use of computers
  • Criteria for software selection
  • Software implementation
  • Data management and knowledge managementProject termination
  • Introduction
  • When to finalise a project?
  • Final steps in the termination of a projectCase studies

Course material

Handbook, presentations (on Toledo).

Format: more information

Lecture.

Evaluatieactiviteiten

Evaluation: Project Management (B-KUL-H24X2a)

Type : Exam during the examination period
Description of evaluation : Oral
Type of questions : Open questions, Closed questions
Learning material : None

Explanation

Assignment per two students with presentation and defense (oral exam) during exam session. Exam timing is coordinated per team of students.

ECTS Cryptography and Network Security (B-KUL-H05E1A)

3 ECTS English 28 First termFirst term
Preneel Bart (coordinator) |  Preneel Bart |  Rijmen Vincent

Aims

  After succesful completion of this course, the student knows

  • the different security goals and how they can be achieved by means of cryptography
  • cryptographic mechanisms: encryption, data authentications, entity authentication, digital signatures
  • the most important symmetric and asymmetric cryptographic algorithms, as well as cryptographic hash functions  (DES, 3-DES, AES, RC4, RSA, DH, DSA, SHA-1, SHA-256/384/512)
  • protocols for key agreement and PKI

Additionally, the student understands how these basic cryptographic mechanisms are used in several modern applications:

  • Internet security mechanisms (SSL/TLS, IPSec)
  • Mobile security (GSM)
  • Electronic payment mechanisms (EMV, electronic purse, electronic cash)

Previous knowledge

Basic knowledge of discrete mathematics (algebra), information theory and communication systems.

Identical courses

H05D9A: Cryptografie en netwerkbeveiliging

Onderwijsleeractiviteiten

Cryptography and Network Security: Lecture (B-KUL-H05E1a)

2.41 ECTS : Lecture 18 First termFirst term

Content

This course explains the basic concepts of cryptology. More in particular, you will learn how cryptographic techniques can protect information against active and passive eavesdropping and how one can authenticate entities. Cryptographic algorithms that are explained include DES, AES, RC4, RSA, Diffie-Hellman, SHA-1, CBC-MAC and HMAC. The role of key management and public-key infrastructures is discussed.
In a second part this knowledge is applied to communications systems, such as GSM and 3GPP, the www (SSL/TLS), email (S/MIME and PGP) and IP (IPsec). The principles of electronic payment systems are explained (EMV, Proton, micropayments). The courses focuses on the development of insight in the basic techniques, and in what they can and cannot do. Applying the knowledge to existing systems is a very important component of this course.

The lectures cover all topics of the course.

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

VTK prints a book containing all the slides that are used during the lectures, some background articles and some supporting text. 

Cryptography and Network Security: Exercises and Laboratory Sessions (B-KUL-H05E2a)

0.59 ECTS : Practical 10 First termFirst term

Content

During the exercises and practica, we cover the topics of two important lectures in more detail:

  • public-key cryptography, and
  • generic attacks on modes of operation of block ciphers and hash functions

 

Course material

The students get a list of exercises (more than are solved during the sessions). 

Format: more information

Three exercise sessions are a preparation for the open-book exam: exercises are solved in the class.

The remaining exercise sessions are replaced by a presentation. Teams of 2 students prepare a presentation of 20 minutes
on a topic selected from a given list of topics related to the lecture. This presentation takes place before start of the exam period.

Evaluatieactiviteiten

Evaluation: Cryptography and Network Security (B-KUL-H25E1a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Presentation
Type of questions : Closed questions, Open questions
Learning material : Course material, Calculator, Reference work

Explanation

The exam result is a weighted score that is determined as follows. The traditional exam during the examination period is taken into account for 85% in the end result and the presentation for 15%.

The exam during the examination period is written. It is an open book exam; additional texts may be consulted. The exam consists of exercises and a closed question, where one has to indicate whether a statement is true or false; if the statement is false, it has to be explained why.

Part of the exercises sessions are replaced by giving a presentation on a recent scientific article related to this course. This presentation is mandatory: not giving a presentation automatically means an automatic failing grade for this course. Students must also attend two sessions of presentation by their fellow students.  

Information about retaking exams

The quotation of the presentation is retained for the 2nd examination period.  If the student wishes to give a (new) presentation for the 2nd examination period, an appointment must be made with the teacher before August 1.

ECTS Measurement Systems (B-KUL-H05F7A)

3 ECTS English 28 First termFirst term

Aims

After succesful completion of this course the student has acquired these competences:

  • The student has an overview of and insight in the working principles of the most widely used sensors and their applications and is capable of choosing the right sensor for a given measurement problem.
  • The student has gained hands-on experience with PC based measurements
  • The student has gained hands-on experience with several commonly used sensors and measurements

Previous knowledge

Basic knowledge of the laws of physics and of electronics, i.e. electricity and magnetism, information and data transfer, applied mechanics.

Is included in these courses of study

Onderwijsleeractiviteiten

Measurement Systems: Lecture (B-KUL-H05F7a)

2.41 ECTS : Lecture 18 First termFirst term

Content

In the lectures, the following topics will be discussed:

  • general concepts: static characteristics of measurement systems (systematic and statistical), accuracy in the steady state, dynamic characteristics of measurement systems and noise
  • specific sensors: resistive, capacitive, inductive, electromagnetic, thermoelectric, elastic, piezoelectric, piezoresistive, electrochemical sensing elements, Hall effect sensors
  • specific measurements: flow, pneumatic measurement systems, heat transfer effects, optical measurement systems and ultrasonic

Course material

Study cost: 76-100 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

A course text will be mad available by VTK

slides will be made available through Toledo

Format: more information

This educational activity consists of nine lectures.

Measurement Systems: Exercises and Laboratory Sessions (B-KUL-H05F8a)

0.59 ECTS : Practical 10 First termFirst term

Content

During the practical sessions, the students will learn to interprete datasheets and control a measurement system using an Arduino microcontroller and the Python programming language. Afterwards, these will be used to perform measurements in various set-ups with different sensors (temperature, force, pressure, flow...). This complements the theoretical background that is given in the lectures.

Course material

The students receive an introductory text about the Arduino, sensor datasheets, an introduction to the Python modules used, a description of the measurement set-ups and a list with questions that need to be answered in every measurement session.

Format: more information

Four laboratory sessions: one about LabVIEW, and three others about measurements in practice. For the latter, a report with measurement results needs to be made, to be handed in at the end of the session. This report will receive a score that is included in the final marks for the course.

Evaluatieactiviteiten

Evaluation: Measurement Systems (B-KUL-H25F7a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Oral, Report
Type of questions : Open questions
Learning material : None

Explanation

The evaluation consists of the exam at the end of the semester, and an evaluation of the reports of the lab sessions with compulsory attendance.

Information about retaking exams

A student who has failed for this course but has passed for the part consisting of the lab reports, does not have to retake the lab sessions. The results of the lab sessions are transferred to the second exam chance, which in that case only consists of a retake of the exam.

ECTS Stochastic Signal and System Analysis (B-KUL-H05I7A)

3 ECTS English 28 Second termSecond term

Aims

After succesful completion of this course the student has acquired these competences:

  • The student understands the extension of the signal processing theory with the processing of random or stochastic signals
  • The student understands the rather theoretical aspects of the course and is able to use the gained knowledge in applications
  • The student has touched a few possible applications of the theory from some examples of mainly audiovisual applications, in part borrowed from research on speech and image processing

Previous knowledge

Basic concepts of probability theory and of digital signal processing.

Identical courses

H05I9A: Stochastische signaal- en systeemanalyse

Onderwijsleeractiviteiten

Stochastic Signal and System Analysis: Lecture (B-KUL-H05I7a)

2.41 ECTS : Lecture 18 Second termSecond term

Content

This is an overview of the topics that are covered:

  • probability theory: random variables, probability distributions, moments, multivariate distributions, functions of random variables, laws of large numbers, parametric estimation, maximum-likelihood estimation, entropy.
  • random processes: moments of random processes, differentiation, integration, ergodicity, the Poisson process, the Wiener process and white noise, stationarity, estimation, linear systems and random processes, power spectrum.
  • optimal filtering: minimum mean square error, bayesian parametric estimation, optimal finite-observation linear filters, Kalman filters.

Course material

Study cost: 11-25 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Parts of the book "Random processes for Image and Signal Processing" by E. R. Dougherty (SPIE/IEEE Press), extra material on Toledo and slides.

Format: more information

Nine lectures in class

Stochastic Signal and System Analysis: Exercises (B-KUL-H05I8a)

0.59 ECTS : Practical 10 Second termSecond term

Content

There are four exercise sessions, covering the most important parts of the course:

  • probability theory
  • parametric estimation, random processes: basics
  • random processes: stationarity, linear systems, power spectrum
  • optimal filtering

Course material

Assignments and solutions are available on Toledo.

Format: more information

There are four exercise sessions, supervised by a teaching assistant.

Evaluatieactiviteiten

Evaluation: Stochastic Signal and System Analysis (B-KUL-H25I7a)

Type : Exam during the examination period
Description of evaluation : Oral, Written
Type of questions : Open questions
Learning material : Course material, List of formulas, Calculator

Explanation

The questions consist of exercises that assess insight.

ECTS Capita selecta ingenieurswetenschappen I.1. (Athens / Summer Course) (B-KUL-H05U5A)

3 studiepunten Nederlands 30 Eerste semesterEerste semester Uitgesloten voor examencontract
Smets Ilse (coördinator) |  N.

Doelstellingen

Inzicht verschaffen in een onderwerp binnen de ingenieurswetenschappen door middel van deelname aan een internationale uitwisseling (ATHENS) of een op voorhand door de programmadirecteur goedgekeurde ‘summer course’. Voor dit opleidingsonderdeel volgt de student een opleidingsonderdeel in het buitenland in het kader van het ATHENS-uitwisselingsprogramma  of een ‘summer course’, mits de programmadirecteur hiervoor op voorhand zijn akkoord heeft gegeven.

De student mag maximaal 1 keer per academiejaar en 2 keer tijdens de masteropleiding deelnemen aan een ATHENS-week. Deelname aan ATHENS is enkel mogelijk na applicatie via de faculteit en selectie door het ATHENS-netwerk. Meer informatie.

 

Begintermen

De kennis en attitudes zoals aangebracht in de bachelor ingenieurswetenschappen.

De student moet voldoen aan de vereiste basiskennis (prerequisites) van het ATHENS-vak dat hij kiest, zoals aangegeven in de course catalogue op de ATHENS inschrijvingswebsite.

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Capita selecta ingenieurswetenschappen I.1. (Athens / Summer Course) (B-KUL-H05U5a)

3 studiepunten : College 30 Eerste semesterEerste semester
N.

Inhoud

Afhankelijk van het opleidingsonderdeel gekozen en toegekend in de buitenlandse instelling na akkoord met de uitwisselingsverantwoordelijke.

Evaluatieactiviteiten

Evaluatie: Capita selecta ingenieurswetenschappen I.1. (Athens / Summer Course) (B-KUL-H25U5a)

Type : Permanente evaluatie zonder examen tijdens de examenperiode
Evaluatievorm : Medewerking tijdens contactmomenten

Toelichting

Dit opleidingsonderdeel wordt geëvalueerd volgens de regels en gebruiken van de gastinstelling waarmee de uitwisseling is gebeurd. De KU Leuven zet deze resultaten om naar PASS/FAIL.
Voor ATHENS-vakken worden, zoals alle andere vakken, in het ISP opgenomen in het academiejaar waarin ze gevolgd zijn.
‘Summer courses’ kunnen enkel gevalideerd worden, indien ze door de uitwisselingsverantwoordelijke voorafgaandelijk goedgekeurd zijn. De student neemt het vak op in het ISP in het academiejaar onmiddellijk volgend op de Summer Course.

Conform het beleid van het ATHENS-netwerk wordt voor ATHENS-cursussen geen 2e examenkans georganiseerd.

Toelichting bij herkansen

 

ECTS Capita selecta ingenieurswetenschappen I.2. (Athens / Summer Course) (B-KUL-H05U6A)

3 studiepunten Nederlands 30 Tweede semesterTweede semester Uitgesloten voor examencontract
Smets Ilse (coördinator) |  N.

Doelstellingen

Inzicht verschaffen in een onderwerp binnen de ingenieurswetenschappen door middel van deelname aan een internationale uitwisseling (ATHENS) of een op voorhand door de programmadirecteur goedgekeurde ‘summer course’. Voor dit opleidingsonderdeel volgt de student een opleidingsonderdeel in het buitenland in het kader van het ATHENS-uitwisselingsprogramma  of een ‘summer course’, mits de programmadirecteur hiervoor op voorhand zijn akkoord heeft gegeven.

De student mag maximaal 1 keer per academiejaar en 2 keer tijdens de masteropleiding deelnemen aan een ATHENS-week. Deelname aan ATHENS is enkel mogelijk na applicatie via de faculteit en selectie door het ATHENS-netwerk. Meer informatie.

Begintermen

De kennis en attitudes zoals aangebracht in de bachelor ingenieurswetenschappen.

De student moet voldoen aan de vereiste basiskennis (prerequisites) van het ATHENS-vak dat hij kiest, zoals aangegeven in de course catalogue op de ATHENS inschrijvingswebsite

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Capita selecta ingenieurswetenschappen I.2. (Athens / Summer Course) (B-KUL-H05U6a)

3 studiepunten : College 30 Tweede semesterTweede semester
N.

Inhoud

Afhankelijk van het opleidingsonderdeel gekozen en toegekend in de buitenlandse instelling na akkoord met de uitwisselingsverantwoordelijke.

Evaluatieactiviteiten

Evaluatie: Capita selecta ingenieurswetenschappen I.2. (Athens / Summer Course) (B-KUL-H25U6a)

Type : Permanente evaluatie zonder examen tijdens de examenperiode
Evaluatievorm : Medewerking tijdens contactmomenten

Toelichting

Dit opleidingsonderdeel wordt geëvalueerd volgens de regels en gebruiken van de gastinstelling waarmee de uitwisseling is gebeurd. De KU Leuven zet deze resultaten om naar PASS/FAIL.
Voor ATHENS-vakken worden, zoals alle andere vakken, in het ISP opgenomen in het academiejaar waarin ze gevolgd zijn.
‘Summer courses’ kunnen enkel gevalideerd worden, indien ze door de uitwisselingsverantwoordelijke voorafgaandelijk goedgekeurd zijn. De student neemt het vak op in het ISP in het academiejaar onmiddellijk volgend op de Summer Course.

Conform het beleid van het ATHENS-netwerk wordt voor ATHENS-cursussen geen 2e examenkans georganiseerd.

Toelichting bij herkansen

 

ECTS Capita selecta ingenieurswetenschappen II.2. (Athens / Summer Course) (B-KUL-H05U7A)

3 studiepunten Nederlands 30 Tweede semesterTweede semester Uitgesloten voor examencontract
Smets Ilse (coördinator) |  N.

Doelstellingen

Inzicht verschaffen in een onderwerp binnen de ingenieurswetenschappen door middel van deelname aan een internationale uitwisseling (ATHENS) of een op voorhand door de programmadirecteur goedgekeurde ‘summer course’. Voor dit opleidingsonderdeel volgt de student een opleidingsonderdeel in het buitenland in het kader van het ATHENS-uitwisselingsprogramma  of een ‘summer course’, mits de programmadirecteur hiervoor op voorhand zijn akkoord heeft gegeven.

De student mag maximaal 1 keer per academiejaar en 2 keer tijdens de masteropleiding deelnemen aan een ATHENS-week. Deelname aan ATHENS is enkel mogelijk na applicatie via de faculteit en selectie door het ATHENS-netwerk. Meer informatie.

 

Begintermen

De kennis en attitudes zoals aangebracht in de bachelor ingenieurswetenschappen.

De student moet voldoen aan de vereiste basiskennis (prerequisites) van het ATHENS-vak dat hij kiest, zoals aangegeven in de course catalogue op de ATHENS inschrijvingswebsite.

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Capita selecta ingenieurswetenschappen II.2. (Athens / Summer Course) (B-KUL-H05U7a)

3 studiepunten : College 30 Tweede semesterTweede semester
N.

Inhoud

Afhankelijk van het opleidingsonderdeel gekozen en toegekend in de buitenlandse instelling na akkoord met de uitwisselingsverantwoordelijke.

Evaluatieactiviteiten

Evaluatie: Capita selecta ingenieurswetenschappen II.2. (Athens / Summer Course) (B-KUL-H25U7a)

Type : Permanente evaluatie zonder examen tijdens de examenperiode
Evaluatievorm : Medewerking tijdens contactmomenten

Toelichting

Dit opleidingsonderdeel wordt geëvalueerd volgens de regels en gebruiken van de gastinstelling waarmee de uitwisseling is gebeurd. De KU Leuven zet deze resultaten om naar PASS/FAIL.
Voor ATHENS-vakken worden, zoals alle andere vakken, in het ISP opgenomen in het academiejaar waarin ze gevolgd zijn.
‘Summer courses’ kunnen enkel gevalideerd worden, indien ze door de uitwisselingsverantwoordelijke voorafgaandelijk goedgekeurd zijn. De student neemt het vak op in het ISP in het academiejaar onmiddellijk volgend op de Summer Course.

Conform het beleid van het ATHENS-netwerk wordt voor ATHENS-cursussen geen 2e examenkans georganiseerd.

Toelichting bij herkansen

 

ECTS Dutch Language and Cultures (B-KUL-H06B4A)

3 ECTS English 40 First termFirst term Cannot be taken as part of an examination contract
De Wachter Lieve (coordinator) |  N. |  De Wachter Lieve (substitute)

Aims

The main aim of this course is to help students acquire basic communicative skills in Dutch (level A1 of the Common European Framework). The course deals with the basic grammar notions and language functions and aims at the mastering of approximately 800 highly frequent words of Dutch. A lot of attention goes to culture with various lectures about Belgium. The course provides extensive practice in listening, reading, speaking and writing. 

Onderwijsleeractiviteiten

Dutch Language and Cultures (B-KUL-H06B4a)

3 ECTS : Lecture 40 First termFirst term
N. |  De Wachter Lieve (substitute)

Course material

R. Devos en H. Fraeters, Vanzelfsprekend, Leuven (Acco), 2008. The multimedia course materials 'Vanzelfsprekend' offer video, audio CDs, texts and exercises, very frequently used language functions and approximately 800 highly frequent Dutch words. The material also includes a lot of cultural information on Belgium and Flanders.

Evaluatieactiviteiten

Evaluation: Dutch Language and Cultures (B-KUL-H26B4a)

Type : Exam outside of the normal examination period

Explanation

There will be a test (writing, speaking, listening and reading) at the end of the course as well as a number of assignments during the course.

ECTS Dutch Language and Cultures (B-KUL-H06U6A)

3 ECTS English 40 Second termSecond term Cannot be taken as part of an examination contract
De Wachter Lieve (coordinator) |  N. |  De Wachter Lieve (substitute)

Aims

The main aim of this course is to help students acquire basic communicative skills in Dutch (level A1 of the Common European Framework). The course deals with the basic grammar notions and language functions and aims at the mastering of approximately 800 highly frequent words of Dutch. A lot of attention goes to culture with various lectures about Belgium. The course provides extensive practice in listening, reading, speaking and writing.

Onderwijsleeractiviteiten

Dutch Language and Cultures (B-KUL-H06U6a)

3 ECTS : Lecture 40 Second termSecond term
N. |  De Wachter Lieve (substitute)

Course material

R. Devos en H. Fraeters, Vanzelfsprekend, Leuven (Acco), 2008. The multimedia course materials 'Vanzelfsprekend' offer video, audio CDs, texts and exercises, very frequently used language functions and approximately 800 highly frequent Dutch words. The material also includes a lot of cultural information on Belgium and Flanders.

Evaluatieactiviteiten

Evaluation: Dutch Language and Cultures (B-KUL-H26U6a)

Type : Exam outside of the normal examination period

Explanation

There will be a test (writing, speaking and reading) at the end of the course as well as a number of assignments during the course. The students will also be asked to answer questions on cultural aspects, based on the attendance of two compulsory lectures on Dutch Language and Belgian Culture.

ECTS Capita Selecta Mathematical Engineering I.1. (B-KUL-H08K8A)

3 ECTS English 20 First termFirst term Cannot be taken as part of an examination contract

Aims

The aim and the content of the course are decided by the Educational Committee Mathematical Engineeering every academic year. The topic can be related to new actual themes in mathematical engineering that have (not yet) been adopted by a course, or the course can be used to help students improve prerequisites.

 

Previous knowledge

Depends on the actual content of the course.

Onderwijsleeractiviteiten

Capita Selecta Mathematical Engineering I.1. (B-KUL-H08K8a)

3 ECTS : Assignment 20 First termFirst term

Content

The content is decided by the programme director and the educational committee every year, and is communicated to the students for whom the content is relevant. The content and working methods are shown in Toledo.

Course material

papers, literature

Format: more information

Depending on the content of the course: college or practical sessions

Evaluatieactiviteiten

Evaluation: Capita Selecta Mathematical Engineering I.1. (B-KUL-H28K8a)

Type : Partial or continuous assessment with (final) exam during the examination period
Type of questions : Open questions
Learning material : Computer

Explanation

The students hand in (progress) reports of their work. This can be a report or software, depending on the topic of the course of that year. There is a final discussion during the examination period in June.

ECTS Image Analysis and Understanding (B-KUL-H09J2A)

6 ECTS English 56 Second termSecond term
Tuytelaars Tinne (coordinator) |  Tuytelaars Tinne |  N. |  Proesmans Marc (substitute)

Aims

Conceptual knowledge of basic algorithms for the processing and interpretation of images.

Previous knowledge

The student must have a basic knowledge of algebra, analysis, geometry, signal processing, pattern recognition and basic notions of machine learning

Is included in these courses of study

Onderwijsleeractiviteiten

Image Analysis and Understanding: Exercises and Practicals (B-KUL-H09I2a)

1.17 ECTS : Practical 20 Second termSecond term
Tuytelaars Tinne |  N. |  Proesmans Marc (substitute)

Content

The exercises and practical sessions elaborate the course knowledge.

Course material

Exercise material is distributed during the sessions or available from Toledo.

Language of instruction: more information

 

 

Format: more information

Guided exercises, partially computer-supported. 

Image Analysis and Understanding: Lecture (B-KUL-H09J2a)

4.83 ECTS : Lecture 36 Second termSecond term
Tuytelaars Tinne |  N. |  Proesmans Marc (substitute)

Content

In this course, the basics of  image processing are acquired and combined with pattern recognition into algorithms for image interpretation. 

Part I: Image processing 
- recording and display 
- sampling and quantization 
- filtering and image enhancement 
- unitary transforms (2D FFT, PCA) 

Part II: Image interpretation
- surface features (color, texture) 
- optical flow and tracking 
- 3D geometry and reconstruction 
- local features and image matching 

Part III: Machine-learning based approaches 
- network architectures for image classification
- dense prediction tasks (semantic segmentation, depth estimation, pose estimation)
- object detection
- advanced topics (image generation, dealing with video, efficient implementations, new trends, ...)
 

Course material

Course notes or slides provided by the lecturers.

Language of instruction: more information

Dutch-speaking students can take the exam in Dutch if they want to.

Format: more information

18 lecture classes: roughly 1/3 on image processing, 1/3 on image interpretation, and 1/3 on machine-learning based methods.

Evaluatieactiviteiten

Evaluation: Image Analysis and Understanding (B-KUL-H29J2a)

Type : Exam during the examination period
Description of evaluation : Oral
Type of questions : Multiple choice
Learning material : None

Explanation

Students get a set of multiple choice questions.
After a short preparation, they are asked to explain and motivate their choices during the oral exam, sometimes followed by a short discussion.
Note that the evaluation is mostly based on the given explanation - just checking the right box is not enough.
There's no correction for guessing.

 

 

ECTS Software for Real-Time Control (B-KUL-H09J9A)

3 ECTS English 28 Second termSecond term

Aims

After this course the student can

  • list the characteristics of different types of real time and embedded systems;
  • describe the typical problems which real time and embedded systems face;
  • describe and compare the solutions that programming languages, operating systems, and software engineering methods offer to address these problems;
  • evaluate the suitability of different programming languages for real-time and embedded systems, and compare these programming languages according to their suitability;
  • describe and compare the typical real time scheduling algorithms.

Previous knowledge

A thorough knowledge of a programming language, including basic principles of object-oriented languages.

Identical courses

H05I5A: Programmatuur voor real-time controle

Is included in these courses of study

Onderwijsleeractiviteiten

Software for Real-Time Control: Lecture (B-KUL-H09J9a)

2.41 ECTS : Lecture 18 Second termSecond term

Content

The course studies the specific characteristics of software for real-time and embedded systems, and investigates which concepts, methods and techniques are suited for the design and implementation of such software.
More specifically the capabilities of different languages for writing software for real-time and embedded systems are discussed and compared: C, Ada, Java, RTSJ. Reliability and concurrency receive special attention.
The necessary support by operating systems (e.g. for scheduling) is studied. The POSIX API as a standard of a RTOS is used as an example. Also writing device drivers is discussed.

Course material

Study cost: 11-25 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

  • Book: Alan Burns, Andy Welling, "real-time Systems and Programming Languages", 2009 Fourth edition, Addison Wesley Longmain 
    ISBN: 978-0-321-41745-9
  • Copies of transparencies

Software for Real-Time Control: Exercises and Laboratory Sessions (B-KUL-H09K0a)

0.59 ECTS : Practical 10 Second termSecond term

Content

Four exercise sessions:

  • Concurrent programming: get experience with Java threads
  • Shared memory and synchronization: gain experience in Java
  • Thread scheduling
  • Real Time Specification for Java

The exercise sessions are hands-on on PC.

Course material

Questions for exercises are uploaded on Toledo. For the rest the same material is used as for the lectures.

Evaluatieactiviteiten

Evaluation: Software for Real-Time Control (B-KUL-H29J9a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions

Explanation

Written exam that assesses insight in the course material, knowledge of terminology and understanding of pieces of code from the book.

If the evaluation indicates that the student has not sufficeintly met one or several of the aims of the course unit, the global result may deviate from the weighted average of all subcomponents.

ECTS Privacy Technologies (B-KUL-H09L2A)

3 ECTS English 35 First termFirst term
N. |  Das Debajyoti (substitute)

Aims

Here are three reasons why you might want to take this course:

First, as engineer you will be designing, implementing, or managing electronic systems and services that in most cases have privacy implications. For example, ambient sensors and biomedical implants collect, process, store, and communicate (sometimes highly sensitive) data related to individuals; the data generated by ubiquitous electronic communications provides detailed insight into people’s activities and lifestyle; and the personalization of multimedia applications and services relies on learning about individuals’ most intimate preferences and adapting to them.

The first objective (O1) of the course is that you are able to identify the privacy concerns that arise in different scenarios. For example, if you are designing a new video-on-demand service for mobile phones, what could go wrong for your future users’ privacy?

The second objective (O2) is that you are able to relate privacy concerns to technical design choices. For example, what privacy risks arise from relying on unique identifiers? or from centralizing the storage and processing of data?

Privacy technologies aim to mitigate privacy concerns. The third objective (O3) is that you understand the principles underlying the design of privacy technologies. For example, anonymity technologies require diversity (of users, uses, attributes, internet subnets, etc.); advanced cryptographic protocols enable sophisticated services (e.g., smart metering) while minimizing the disclosure of data beyond what seems intuitively possible; and differential privacy ensures that queries to database of personal records cannot be used to determine if a particular individual record is included in the database.

Second, you will deal with privacy issues not only as an engineer but also as an individual and as a member of society. As an individual, you use a variety of services: mobile communications, online shopping, search engines such as Google, social media such as Facebook or Twitter, etc. The fourth objective of this course (O4) is that you become aware of what privacy issues are associated to the use of different services, what are your basic (legal) rights concerning privacy and data protection, and what technologies you can use to mitigate your exposure to privacy risks. As a result of this, you will be able to form your own informed opinions on how privacy issues should be addressed in our increasingly technological society. Many of these privacy issues are at the heart of ongoing debates whose outcome will have an influence on how society is shaped: Is the tracking of Internet users necessary for the economic sustainability of the Internet? Might profiling and personalization lead to social sorting and discrimination? Should certain content be censored? Should we have real name policies in social media to combat harassment? Should user communications be stored for long periods of time for the purpose of law-enforcement investigations? Are we building an unprecedented mass surveillance infrastructure, or are overblown privacy concerns an obstacle to data-driven innovation?

Finally, the course is heavily based on recent research. By participating in this course you will get a first hand experience of what research is like. The sixth objective (O5) is that you learn to read scientific articles, as well as to develop and present your own ideas. 

 

Previous knowledge

Ideally, students have a basic background in:

  • probability theory and statistics: computing probabilities in basic models; understanding what is joint probability, conditional probability, random variable; knowing basic distributions (uniform, exponential, binomial); etc.
  • information theory: familiarity with concepts such as entropy and mutual information
  • cryptography, computer and network security: basic knowledge of cryptographic primitives such as symmetric key encryption, hash functions, and digital signatures; and of internet protocols, such as TLS or SSH.

Students lacking parts of this background will also be able to follow the course – with a bit of preparation they can quickly be up to speed with the basic background knowledge required.

Onderwijsleeractiviteiten

Privacy Technologies: Lectures (B-KUL-H09L2a)

1.8 ECTS : Lecture 14 First termFirst term
N. |  Das Debajyoti (substitute)

Content

This course provides an introduction to privacy technologies. We will explain the various types of privacy risks and introduce a range of existing privacy technologies that address these risks. These include:        

  • cryptographic protocols with applications to privacy, including: private information retrieval, oblivious transfer, anonymous e-cash, anonymous authentication, and private search.        
  • privacy engineering, including: privacy in agile frameworks, introduction to anonymous communication systems.
  • database privacy & data anonymization, including: k-anonymization, re-identification algorithms, and differential privacy.       
  • ML/AI privacy, including: privacy preserving ML/AI and ML/AI for privacy.
  • web privacy, including: web tracking techniques, cookies, device fingerprinting.
  • legal aspects of privacy, including: GDPR and Human Rights legal frameworks.

 

Course material

Slides, notes, and research articles for further reading.

Format: more information

The lectures are interactive. Students are expected to ask and answer questions and actively participate in class discussions.

Privacy Technologies: Exercises and Laboratory Sessions (B-KUL-H09L3a)

1.2 ECTS : Practical 21 First termFirst term
N. |  Das Debajyoti (substitute)

Content

There will be four exercise sessions in total.

Session 1: Privacy scandals session (1 point)

In this session students give a presentation (of a few minutes) on a privacy scandal of their choice. To prepare for this session, the student must search online news and documentation of a high-profile incident of the last year that violated the privacy of a person or a group of people. Examples of incidents may include data breaches, unlawful data saring/use practices by organizations, or any other event. During the session the student should explain the story of the incident, the reasons (e.g., some security vulnerability), and the consequences.  

Sessions 2 & 3: Assignment feedback sessions (no points)

In these two sessions students work in groups of about 4 people. Each student takes about half an hour to explain to the others in the group the topic they have chosen for their assignment and their approach to addressing that problem. Students discuss and give feedback to each other on their respective assignments. 

Session 4: Assignment presentation session (4 points)

In this session students will have a few minutes to present their assignment to the lecturers and TAs of the course, who may ask questions and give feedback to the students. The students will still have some days to finalize the assignment incorporating the received feedback and addressing issues identified during the presentation. 

Course material

Scientific articles, software tools. 

Evaluatieactiviteiten

Evaluation: Privacy Technologies (B-KUL-H29L2a)

Type : Continuous assessment without exam during the examination period
Description of evaluation : Paper/Project, Report, Presentation
Type of questions : Open questions
Learning material : Course material

Explanation

The final grades in a scale of 20 points will be computed as follows:     

  • [1 pt] For the presentations of the privacy scandals exercise session (see exercise sessions). 
  • [4 pt] For the assigment presentation (see exercise sessions). 
  • [15 pt] For the final written assignment. 

The assignment is a paper that motivates, designs and evaluates a privacy-enhanced system, including: 

  • Define a functionality, system model, assumptions, privacy/security properties, and threat model
  • Define an architecture combining building blocks (technologies, protocols) seen in the lectures 
  • Discuss/argue which desired security/privacy properties are achieved and which are not achieved 

Students can work either individually or in pairs to write an paper of between 3500 and 4500 words. The final version of the paper must be submitted before the start of the January examination period. The presentation of the assigment takes place in December so that students can receive feedback before they finalize and submit their final paper. 

 

Information about retaking exams

In the second chance, all 20 points are evaluated on the basis of the written assignment. The deadline for submitting the assignent is BEFORE the start of the examination period (last day before the examination period starts). 

ECTS e-Security (B-KUL-H09L4A)

3 ECTS English 28 First termFirst term

Aims

After succesful completion of this OPO, the student

  • understands the basic types of access rights;
  • knows the following important security policies and policy frameworks: Bell-LaPadula, Biba, Chinese Wall, Clark-Wilson;
  • understands the basic concepts of Unix security, Windows security, database security, software security.

In addition, the student sees how modern computer applications are plagued by re-incarnations of old security problems. In particular, the student

  • understands various network security problems (TCP SYN flooding, DNS cache poisoning);
  • understands web security problems and solutions (XSS, CSRF, same-origin policies, sandboxing).

Finally, the student can assess the risks and threats of a given scenario, design a security policy and propose security mechanisms to implement the security policy.

Previous knowledge

The students understand a standard computer architecture and its operation. The students can read and understand code snippets written in a modern programming language (Java, C).

Onderwijsleeractiviteiten

e-Security: Lectures (B-KUL-H09L4a)

2.41 ECTS : Lecture 18 First termFirst term

Content

The course covers theoretical concepts like access control matrices and security policies. We also study the design of secure systems and security evaluation principles.

The theory is illustrated by classical examples like Unix security, Windows security, software security, and by more modern examples like network security, web security, e-commerce, digital right management (DRM).

The lectures cover all the topics of the course.

Course material

Study cost: 51-75 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

This course is new; there are no course notes yet. We'll follow closely the following book:

Dieter Gollmann, Computer Security (3rd edition), Wiley, ISBN 978-0-470-74115-3.  

The students will get copies of the slides. There will be a list of references.

Language of instruction: more information

The teacher is a native Dutch speaker. Students may choose to communicate in Dutch instead of English.

e-Security: Exercises and Lab Sessions (B-KUL-H09L5a)

0.59 ECTS : Practical 10 First termFirst term

Content

The lab sessions illustrate the concepts covered in the lectures:

  • Web security and database security
  • Network enumeration and network security challenges
  • Software security
  • Operating system security and privilege escalation

 

Course material

The students are to bring their laptop computers. Images of virtual machines will be provided in order to run the experiments.

Format: more information

The students will deliver a written report. (no presentation required)

Evaluatieactiviteiten

Evaluation: e-Security (B-KUL-H29L4a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written
Type of questions : Open questions

ECTS Engineering & Entrepreneurship (B-KUL-H09P4A)

6 ECTS English 69 Second termSecond term Cannot be taken as part of an examination contract

Aims

The course explains and illustrates the role of leadership and technology in the entrepreneurial process.

  • The student can explain the key role of technology and engineering in entrepreneurship
  • The student is able to take advantage of market opportunities by planning, organizing, and employing several types of resources.
  • The student is able to clarify the role of and generate a business plan for an existing as well as a new to start-up company.
  • The student can clarify how different units within the company interact and how the company should position itself within a given market, based on the participation during the business games and the testimonies by the entrepreneurs to.
  • The student can explicate the product development cycle and more specifically the creative phase following the need recognition and problem formulation stages. In this phase design concepts need to be conceived and assessed.
  • The student can indicate the techniques of Business Strategic Dialogues and the role of leadership in this.

Previous knowledge

Students are not allowed to follow the course H09Q1A ‘Leadership and Strategic Management’ (3 ECTS) nor H04V2A ‘Ontwerpmethodologieën’ (6 ECTS) when they subscribe this course.

Is included in these courses of study

Onderwijsleeractiviteiten

Business Simulations (B-KUL-H09P5a)

1.5 ECTS : Assignment 30 Second termSecond term

Content

The ola consists of two games:

 

  • concurrent engineering game: this business simulation game makes students familiar with the important influence of organizational structures on the performance of project teams with parallel, interacting task responsibilities. The exercise consists of a 4 hours competitive product development effort set in a real life production facility.
  • business game: during this three day business game students have to organize themselves in teams or companies. They create a vision, set goals for their company, translate them in the normal activities of a company: hiring people, buying raw material, investing in machines, price setting, marketing, selling and delivering the products, production planning, etc. At the end of the game during a formal session what they hoped to reach and what has been reached is discussed.

Course material

Handouts made available to the students  before the start of the games.

Format: more information

Interactive business simulation games: presence is obligatory.

Is also included in other courses

H09Q1A : Leadership and Strategic Management

Strategic Management (B-KUL-H09P8a)

1.5 ECTS : Lecture 15 Second termSecond term

Content

1. Leadership:How to define,types of profiles(inspirational,organisational),style
2. Strategic Dialogues: Vision and Strategy as a tool to aline teams and lead the team to common goals.Technique of defining actual situation against strategic desired position (Ist/Soll) and definition of action programs to get there.
3. What to do in global crises: short time survival to reach long term objectives (use of operational KPI's)
4. Culture of enterpreneurship and commitment
5. Why?(reason to exist),how?(values),what?(action plans)
6. How evaluate (choose) the team and reward it?
7. Priority setting (people,profit,planet?)
8. Translation and communication of vision/strategy to affiliates and workfloor
9. Role of innovation10. Case study of a company in Belgium

Course material

Handbook, texts and presentations

Format: more information

Mixture of classes and seminars

Is also included in other courses

H09Q1A : Leadership and Strategic Management

Creativity and Decision Making for Product Development (B-KUL-H0T37a)

2 ECTS : Lecture 12 Second termSecond term

Content

1. Characteristics of design activities and systematic design procedures

2. Creativity methods: including

  • Lateral thinking
  • Brainstorming
  • Synectics
  • Biomimicry, biologically inspired design
  • Combinatorial concept generation
  • Morphological analysis

      and creativity quantification

3.   Design by Analogy and Systematic biologically inspired design

4.  Theory of Inventive Problem Solving :TIPS / TRIZ

5.  Open innovation and lead users

6.  Design evaluation methods and decision theory

  • Design axioms
  • Decision matrices
  • Decision theory
  • Multi-criteria decision making

Course material

Handouts and selected articles

Technology & Entrepreneurship: Case Studies (B-KUL-H0T38a)

1 ECTS : Lecture 12 Second termSecond term

Content

Testimonies on the role of engineering and technology in the start-up of technology spin-offs. Leading entrepreneurs of technology spin-off companies will be invited to contribute to this seminar lectures.

Course material

Byers, T.H. Dorf, R.C., & Nelson, A.J. (2010). Technology ventures: From idea to enterprise (3rd ed.). New York: McGraw-Hill.

Handouts of the presentations.

Evaluatieactiviteiten

Evaluation: Engineering & Entrepreneurship (B-KUL-H29P4a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Paper/Project, Presentation, Participation during contact hours
Type of questions : Open questions

Explanation

 

  • ‘Business Simulations’: continuous assessment based on participation
  • ‘Strategic Management’ and ‘Creativity and decision making for product development’: written exam during the exam session, open questions
  • ‘Technology & Entrepreneurship: case studies’: short paper on a case study
  • One of the business game takes place during three consecutive days during the Easter holidays, this game is graded based on participation.

Not participating in one of the diffferent parts results in failing this course. There is no possibility to take a second exam session for the games in September.

If the faculty decides that the business games cannot go ahead in their current form, compulsory attendance will be waived. The business games will then not be included in the assessment of this course.

 

 

 

Information about retaking exams

You cannot retake the business games in the September exam session, since they exist of continuous assessment. However, you can retake the course modules ‘Strategic Management’, ‘Creativity and Decision Making for Product Development’ and ‘Technology & Entrepreneurship’.

ECTS Computer Algebra for Cryptography (B-KUL-H0E74A)

3 ECTS English 38 Second termSecond term Cannot be taken as part of an examination contract

Aims

Computer algebra is the area of computer science that develops tools and algorithms for symbolic and therefore exact computations which are fundamental for cryptography and coding theory. The approach and algorithms are totally different from numerical analysis that provides algorithms for approximate solutions. The goal of this course is to give a thorough introduction to computer algebra algorithms and their complexity, motivated by applications in engineering with an emphasis on applications in cryptography. At the end of the course the student should be able to:

  • Understand and explain how theorems from algebra can be used in the design of algorithms and how their complexity is influenced by the theory.
  • Perform an asymptotic complexity analysis and understand the difference with practical efficiency and the need for crossovers between multiple algorithms.
  • Design and implement computer algebra algorithms in Magma to solve real life problems in engineering, more particular cryptography, and evaluate their efficiency. 

  • Consult and comprehend recent scientific literature in computer algebra and assess the results described. 


Previous knowledge

Basic knowledge of algebra (e.g. H01G5A) including finite fields, polynomial rings and ideals.

Identical courses

H0E78A: Computeralgebra voor cryptografie

Onderwijsleeractiviteiten

Computer Algebra for Cryptography: Lecture (B-KUL-H0E74a)

2 ECTS : Lecture 18 Second termSecond term

Content

  • Introduction and overview of the course. Fundamental algorithms, complexity notation, addition and multiplication of numbers and polynomials, GCD, Chinese Remainder Theorem.

  • Fast multiplication and division: evaluation/interpolation approach, Karatsuba, Toom- Cook, DFT & FFT, Schönhage & Strassen, quotient & remainder via Newton iteration. Applications: Shamir secret sharing, ring-LWE cryptosystem.
  • Euclid’s algorithm and resultants: XGCD algorithm, modular arithmetic, resultant, modular GCD algorithm.
Applications: rational approximation, continued fractions, intersections of curves, implicitization of parametric curves.
  • Primality testing and factorisation algorithms: Fermat’s test, Carmichael numbers, Miller- Rabin, Pollard Rho, difference of squares, group based methods (p-1 method, elliptic curve method), introduction to quadratic sieve and number field sieve.
Applications: RSA and Paillier cryptosystems.
  • Short vectors in lattices: lattices, lattice minima, Minkowski’s theorems, Gaussian heuristic, lattice reductions algorithms (LLL, BKZ), ideal lattices.
Applications: short dependence relations, breaking knapsack cryptosystems, NTRU, small roots of modular polynomials, Coppersmith’s algorithm, security of RSA with small exponent.
  • Polynomials: fast evaluation & interpolation, factorisation (square-free, distinct degree, equal degree), Berlekamp’s algorithm, Hensel lifting and factorisation of polynomials over Z.
Applications: solving approximate GCD problem, error correcting codes.
  • Gröbner bases: polynomial ideals, monomial orders, division with remainder, Hilbert’s basis theorem, Gröbner bases and S-polynomials, Buchberger’s algorithm, degree of regularity.
Applications: solving systems of non-linear equations, algebraic attacks on multivariate cryptography, robotics (inverse kinematics problem).

Course material

  • A full set of course notes written in English will be provided. 

  • Main additional reference: Modern Computer Algebra (3rd edition) by von zur Gathen and 
Gerhard. 

  • Applications reference: Algorithmic Cryptanalysis by Antoine Joux.

Format: more information

Goal of lectures: formulate computational problem motivated by application example, provide possible solution strategies, recall or introduce necessary theory from algebra (without proof), deduce algorithms and compute their complexity. Discuss efficiency in practice and possible further improvements, provide an overview of the state of the art and indicate differences with the algorithms described in lectures. 
Focus is on how (mostly) simple theorems from algebra lead to efficient algorithms, their complexity, practical performance and applications.

Computer Algebra for Cryptography: Exercises and Laboratory Sessions (B-KUL-H0E75a)

1 ECTS : Practical 20 Second termSecond term

Content

There will be 8 supervised exercise sessions of 2.5 hours each using the computer algebra system Magma. The goal is to familiarize the students with real implementations of the algorithms described in the lectures and to assess their efficiency on practical problems. 
Each student (individually) will have to solve two medium-sized projects (4 exercise sessions per project). These projects will be based on open research questions where (partial) solutions can be devised using the algorithms described in the lectures. The student will need to consult existing literature (references will be provided), devise his/her own solution, implement it in the Magma language and hand in a written report on the solution strategies and implementation results.

Course material

An introduction to the Magma language will be provided and the full Magma manual can be accessed online.

Problem sheets and pointers to the literature will be provided beforehand so the students can familiarize themselves with the projects.

Format: more information

The first 0.5 hours will consist of a quick overview of the Magma implementations of the algorithms described in the previous lectures. The remaining time the student has the opportunity to work on solving the two projects (4 sessions per project) and ask questions/feedback from the supervisor.

Evaluatieactiviteiten

Evaluation: Computer Algebra for Cryptography (B-KUL-H2E74a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Oral, Paper/Project
Type of questions : Open questions
Learning material : Course material, Reference work

Explanation

The evaluation is based on two projects, where both Magma code and a short project report are required detailing the approach and the results obtained. The deadline for the first project is set at the start of the 5th exercise session and the deadline for the second project is at the end of the course.

There will an oral exam where the student is asked to explain his/her work, to test the student’s insights and to provide feedback. Each project accounts for 35% of the final mark (quality and efficiency of the code provided and an evaluation of the written report), and the oral exam for the remaining 30%.

Information about retaking exams

The evaluation consists of continuous assessment on the basis of the two projects described above. If the student fails during the 1st exam opportunity, he/she will have to solve a third project and explain it during a new oral exam.

ECTS Model Predictive Control (B-KUL-H0E76A)

4 ECTS English 35 First termFirst term Cannot be taken as part of an examination contract

Aims

This course aims at presenting an overview of real-time optimization-based control of dynamical systems, also known as model predictive control (MPC). It presents system-theoretic properties of MPC, such as stability, invariance, offset-free control, regulation and tracking, as well as numerical algorithms for solving the resulting optimal control problems. The focus is on both linear and nonlinear, continuous-time and discrete-time systems in state-space form. A number of case studies is presented, ranging from attitude and navigation control of quadcopters, collision avoidance for autonomous vehicles and hybrid vehicle control to multiperiod portfolio optimization, power dispatch in smart grids.

Finally, the student will gain both a deep theoretical understanding of the main principles as well as practical experience with MPC through an assignment consisting of a series of theoretical exercises and an MPC design project applied to autonomous racing. 

Previous knowledge

optimization, numerical linear algebra, basic systems & control theory

Onderwijsleeractiviteiten

Model Predictive Control: Lecture (B-KUL-H0E76a)

2 ECTS : Lecture 20 First termFirst term

Content

  • Introduction to Optimal control
 modeling for control; state-space models; discrete-time optimal control; linear & nonlinear optimal control; dynamic programming; direct methods for optimal control. 

  • Model predictive control
 receding horizon principle; Lyapunov stability; constraint satisfaction & invariance; tracking and offset free MPC; robust & stochastic MPC; modeling hybrid systems and logic. 

  • State estimation
 (extended) Kalman filtering; moving horizon estimation; output feedback MPC. 

  • Numerical Optimal control
 active set & interior point methods; sequential quadratic programming; augmented Lagrangian methods; proximal algorithms; mixed-integer optimization. 

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Model Predictive Control: Exercises and Laboratory Sessions (B-KUL-H0E77a)

2 ECTS : Practical 15 First termFirst term

Content

The sessions consist of exercises on the topics from the lectures. An assignment of a simulation based project providing practical experience with MPC using the tools from the exercise sessions is given during the first half of the semester. This assignment will be graded.

Evaluatieactiviteiten

Evaluation: Model Predictive Control (B-KUL-H2E76a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions
Learning material : Course material

Explanation

The grading consists of two parts: a written exam (theoretical) and a grade for the assignment based on a written report.

ECTS Numerical Simulation of Differential Equations (B-KUL-H0M80A)

6 ECTS English 32 First termFirst term Cannot be taken as part of an examination contract
Samaey Giovanni (coordinator) |  Feppon Florian

Aims

Due to their complexity, the differential equations that engineers and scientists are confronted with usually do not allow for an exact analytical solution. One is then obliged to compute approximate numeral solutions. Via some characteristic model problems, the students in this course learn how to transform a differential equation into a discrete numerical problem that can be solved on a computer.  After this course, the student will be able to:
- describe standard discretisation techniques for ordinary differential equations (linear multistep methods, Runge-Kutta methods) and partial differential equations (finite differences,finite elements and finite volumes)
- analyse the convergence properties of these methods (consistency, stability, convergence, accuracy) and variants
- explain how different properties of the method affect computational cost (implicit vs. explicit methods, solution of nonlinear systems)
- discuss the suitability of these methods for specific types of problems (stiff or geometric ordinary differential equations; parabolic, hyperbolic and elliptic partial differential equations)
- implement these methods for a concrete application, and compare and explain their behaviour in terms of the properties of the method and the problem under study.

Previous knowledge

The student should have a basic knowledge of calculus, including differential equations, and numerical mathematics.

Identical courses

H03D7A: Numerieke simulatie van differentiaalvergelijkingen

Onderwijsleeractiviteiten

Numerical Simulation of Differential Equations: Lecture (B-KUL-H0M80a)

4.5 ECTS : Lecture 24 First termFirst term

Content

Part I:  Ordinary differential equations

  • Forward and backward Euler method, trapezoidal rul
  • Order of a method / consistency / convergence
  • Stiffness, stability
  • Geometric integration
  • Higher-order methods: linear multistep methods and Runge-Kuttamethods
  • Splitting methods

Part II: Elliptic partial differential equations

  • Finite differences: order and convergence
  • Finite elements
  • Spectral methods

Part III: Parabolic partial differential equations

  • Finite differences for the one-dimensional heat equation
  • Finite differences for higher-dimensional parabolic problems
  • Finite elements and spectral methods for parabolic problems

Part IV: Hyperbolic partial differential equations

  • Finite difference for the linear advection equation
  • Non-linear hyperbolic conservation laws and finite volume methods

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Own course material, distributed via Toledo.

Format: more information

Lectures, exercise sessions and practical assignments are integrated in 20 contact moments of 2h. 

These contact moments are prepared by the students via short implementation assignments and numerical experiments.  These assignments are the starting point for the instruction of new material.

 

Numerical Simulation of Differential Equations: Exercise Sessions and Projects (B-KUL-H0M81a)

1.5 ECTS : Practical 8 First termFirst term

Content

Lectures, exercise sessions and practical assignments are integrated in 20 contact moments of 2h. 

These contact moments are prepared by the students via short implementation assignments and numerical experiments.  These assignments are the starting point for the instruction of new material.

 

Course material

Handbook/articles and literature/Toledo.

Evaluatieactiviteiten

Evaluation: Numerical Simulation of Differential Equations (B-KUL-H2M80a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions
Learning material : Course material

Explanation

For more information on question types and grading, see Toledo/

Information about retaking exams

If the student failed the practicals, he/she will get a new assignment.

ECTS Methods and Algorithms for Advanced Process Control (B-KUL-H0M82A)

6 ECTS English 60 Second termSecond term Cannot be taken as part of an examination contract

Aims

This course provides an overview of the most important control methods currently in use. After an elaborated introduction on classical control technology, the course focuses on state feedback control. The students are taught the principles of model-based predictive control, as well as techniques for state estimation. The advantages and disadvantages of the different techniques are presented to give the students a view of which technique is the most appropriate for a given
control problem. At the end of the course the students will be able to address complex multivariable control problems by using state-space feedback techniques and model predictive control strategies.

Previous knowledge

Skills: the student should be able to analyze, synthetisize and interpret
Knowledge: Systems and control theory and linear algebra.

Identical courses

H03E8A: Computergestuurde regeltechniek

Is included in these courses of study

Onderwijsleeractiviteiten

Methods and Algorithms for Advanced Process Control: Lecture (B-KUL-H0M82a)

3 ECTS : Lecture 40 Second termSecond term

Content

1. Introduction
1.1. Brief review of Classical control theory
1.2. Classical Control vs. Modern Control
1.3. Real-life control examples
1.4. System and Models: Taxonomy
1.5. System modeling

2. State-space representation
2.1. Introduction
2.2. Transfer function matrix and impulse response
2.3. Linearization of nonlinear systems
2.4. Discretization of continuous-time models
2.5. Geometric properties of linear state-space models
2.6. Similarity transformation
2.7. Controllability
2.8. Observability
2.9. The Popov-Belevitch-Hautus tests (PBH)
2.10. Stability, Stabilizability  and Detectability
2.11. Kalman decomposition and minimal realization
2.12. Input/output properties of state-space models

3. State feedback controllers
3.1.  Introduction
3.2. Pole Placement method
3.3. Linear Quadratic Regulator (LQR)

4. Reference Introduction
4.1. Introduction
4.2. Reference Input - full state feedback
4.3. Integral control and Robust Tracking

5. State estimators
5.1. Open-loop vs. Closed-loop estimators
5.2. Estimator design via Pole Placement
5.3. Optimal Estimator - Kalman filter

6. Compensator design
6.1. Generalities
6.2. Separation Principle - Pole placement
6.3. Linear Quadratic Gaussian (LQG) control
6.4. Stochastic separation principle

7. Model Predictive Control  (MPC)
7.1. Introduction
7.2. Receding horizon principle
7.3. Different MPC formulations
7.4. Terminal cost

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

The digital version of the course text (slides) is available in Toledo. 

Format: more information

Two lectures a week of two hours each.

Methods and Algorithms for Advanced Process Control: Exercises and Laboratory Sessions (B-KUL-H0M83a)

1 ECTS : Practical 20 Second termSecond term

Content

  • 4 exercise sessions in a PC room. In 2 of these sessions the students are introduced to the design control problems that they have to tackle and from which they have to write reports.
  • Lab sessions (practicum) where the students have to deal with the real-time implementation of a control strategy for a mechanical setup

Course material

All the info about the exercise sessions and the practicum is available in Toledo.

Methods and Algorithms for Advanced Process Control: Project (B-KUL-H0M84a)

2 ECTS : Assignment 0 Second termSecond term

Evaluatieactiviteiten

Evaluation: Methods and Algorithms for Advanced Process Control (B-KUL-H2M82a)

Type : Exam during the examination period
Description of evaluation : Oral

Explanation

The evaluation consists of two assignments. In the first assignment, the students have to design and implement control systems within a simulation environment. In the second assignment, they have to develop a real-time control strategy for a mechanical setup available in the Lab. At the end of the course, the students have to present two written reports and they have to defend their design choices and results in an oral open-book exam. The students also have to answer some theoretical questions about the course. The assignments for the second examination period are analogous to those from the first one, and the evaluation criteria are the same.

ECTS Case Studies: Mathematical Engineering (B-KUL-H0M85A)

3 ECTS English 51 Second termSecond term Cannot be taken as part of an examination contract
Meerbergen Karl (coordinator) |  De Moor Bart |  Meerbergen Karl

Aims

- Acquiring insight in applications of mathematical engineering in business, industry and in scientific research, in which the matter of different courses is combined
- Learning to make oral and written reports on a technical problem and its solution

Previous knowledge

- Skills: the student should be able to analyze, synthesize and interpret
- Knowledge: the student should have basic knowledge of Technical mathematics, Numeral simulation of differential equations, System identification and modelling, Optimization, Control theory.

Identical courses

H03E5A: Gevallenstudies: wiskundige ingenieurstechnieken

Onderwijsleeractiviteiten

Case Studies: Mathematical Engineering (B-KUL-H0M85a)

3 ECTS : Practical 51 Second termSecond term

Content

Experts from the professional world and scientific research will give seminars on topics that link up with the subject matter of the first master year. The students will look up information on one topic, work out one of its aspects and report on it in a written or oral form.

Format: more information

Lessons given by external speakers; discussion with the speakers; debates organized by the students.

Evaluatieactiviteiten

Evaluation: Case Studies: Mathematical Engineering (B-KUL-H2M85a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Paper/Project, Presentation
Type of questions : Open questions

Explanation

The students are expected to actively participate in the seminars. They will report in oral and written form on one seminar.

ECTS Scientific Software (B-KUL-H0M86B)

5 ECTS English 45 First termFirst term Cannot be taken as part of an examination contract
Meerbergen Karl (coordinator) |  Meerbergen Karl |  Nuyens Dirk

Aims

  • Familiarizing the students with the characteristics that are typical for scientific software.
  • Familiarizing the students with existing numerical software libraries and teaching them to choose from this software.
  • Teaching the students to independently design scientific software.

Previous knowledge

Skills: the student should be able to analyze, synthesize and interpret.
Knowledge: a basic course in programming, Object-oriented programming, Numerical mathematics.

Identical courses

H03F0B: Technisch-wetenschappelijke software

Onderwijsleeractiviteiten

Scientific Software: Lecture (B-KUL-H0M86a)

3 ECTS : Lecture 20 First termFirst term

Content

1. Introduction
- Characteristics of scientific software

2. Working with real numbers
- deeper study of IEEE 754 (not limited to number representation, but also dealing with less known aspects such as calculation rules and floating point exceptions)
- multiple-precision arithmetic
- interval arithmetic

3. Languages for scientific applications
- Comparative study of higher programming languages from the point of view of scientific calculations (support of IEEE754, floating point exceptions, matrix representation, intrinsic speed limitations...)
- Object-oriented design in non-pure OO-languages
- Efficiency as attention point during the design and implementation of software
- More thorough study of languages designed for calculations.

4. Working with real computers
- Performance improvement on 1 processor (with multiple calculating units)
- Benchmarking

5. Working with real algorithms
- Testing and evaluating numerical software
- The compromise between reliability and efficiency
- Implementing machine-dependence in portable software

6. Directions in mathematical software
- Overview of various sources
- Use of libraries versus 'tools'

Course material

Study cost: 51-75 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

Textbook/articles and literature/course text/Toledo
- Book "Accuracy and reliability in scientific software", IFIP WG 2.5 Project 68.
- Educating programmes by Cornell Theory Center

Scientific Software: Exercises and Laboratory Sessions (B-KUL-H0M87a)

2 ECTS : Practical 25 First termFirst term

Content

During the guided exercise sessions, the students become familiar with different aspects of scientific software. The guided exercise sessions serve as a springboard for the home work assignments.

Course material

Manuals Fortran 95/2003 and C++.

Evaluatieactiviteiten

Evaluation: Scientific Software (B-KUL-H2M86b)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Oral, Report, Take-Home
Type of questions : Open questions

Explanation

The evaluation is based on home works and a graded exercise session. Each home work is connected to the lectures and exercises. The student must solve the tasks individually and submit them before a given deadline. The purpose of the exam in the examination period in January is to give feedback to the student and to ask the student to explanation his/her work. The final score is based on the reports of the home works and the additional explanations. If the evaluation indicates that the student has not sufficiently met one or several of the aims of the course unit, the global result may deviate from the weighted average of all subcomponents.

A student who obtains 6/20 or less for one third or more of home works cannot succeed.

2nd exam opportunity:

The evaluation consists of a form of continuous assessment on the basis of the home works. Therefore, there is no possibility for a reexamination in the August/September examination period. Spreading the work over the academic year and the September examination period is also excluded.

Only in case of a proven "force majeure", there will the opportunity to hand in all or part of the assignments under a modified form.

Information about retaking exams

 

ECTS Master's Thesis (B-KUL-H0M89A)

24 ECTS English 720 Both termsBoth terms Cannot be taken as part of an examination contract Cannot be taken as part of a credit contract
Samaey Giovanni (coordinator) |  N.

Aims

The student
… has initiated an original research project (original in the sense that the student has generated (partly) new knowledge) .
... has acquired state of the art knowledge on the subject of the research project.
... formulates a correct and clear problem statement.
... is up to date with recent findings in the area of the subject of the research project and can assess their relevance for the solution of the problem
... designs a research plan, using the best available techniques (based on information found in scientific literature).
… analyses and interprets the results obtained.
… has a critical attitude in the interpretation of the results obtained.
… takes into account the need for optimization  (context and boundary conditions) and the existence of uncertainties that have an impact on the boundary conditions.
… can outline the results of the project in a coherent, correct and clear way using a correct scientific language and a clear lay-out of the text, citations, tables and figures meeting all formal requirements….
… has a fair academic attitude towards referencing sources.
… brings the project to a close in a set of conclusions situating the results obtain in the state of the art context
… can present the results of the project, taking into consideration important presentation skills such as the outline of the scientific context, a coherent structured presentation, correct language, respect for timing.
… can answer in a scientific correct language to questions from both fellow students and researchers.
… assumes a critical, reflective learning attitude, committed  to the project, independent and if appropriate a good team player.

Previous knowledge

The student should have taken sufficient courses of the specialised technical education such that sufficient competences have been acquired to do the research work.

Preliminary conditions:  According to education regulations. (From 2023 on: the student can get a topic assigned when 72 or fewer credits are still open.)

Order of Enrolment

72

Is included in these courses of study

Onderwijsleeractiviteiten

Master's Thesis (B-KUL-H0M89a)

24 ECTS : Master's thesis 720 Both termsBoth terms
N.

Content

The precise content of the master thesis depends on the topic agreed upon, but in any case it will be a research or design work that takes place either in one of the research laboratories to which the professors of the master programme are connected, or in an industrial environment.

Evaluatieactiviteiten

Evaluation: Master's Thesis (B-KUL-H2M89a)

Type : Continuous assessment without exam during the examination period

Explanation

A thesis is evaluated by a jury of at least 3 people: the promotor(s), the (potential) daily supervisor and two or more assessors. This will occur on the basis of 3 aspects:
1. The process: the work done during the year (independence, critical sense, inventivity, creativity, grade of difficulty)
2. The product: the final project and/or the text (scientific contents, style, language, care, readability, structure), as well as the poster
3. The presentation and oral questioning (style, language, care, structure, completeness, use of time)

The thesis has to be subitted electronically and on paper. Guidelines are available on the programmes and faculty websites.

ECTS Religions (B-KUL-H0N82A)

3 ECTS English 20 Second termSecond term

Aims

Students aim at  

  • clarifying the functioning of religions and world views, especially the Christian religion, into culture and society;  
  • analyzing which anthropological stances and worldviews are present in society and culture (e.g. in media, health care, economy, technology, education) and critically reflecting on it;  
  • showing, explaining and illustrating the particularity of world views and religions, especially the Christian worldview;  
  • applying theoretical views from theology and religious sciences into actual societal debates;  
  • learning to know religious and ethical themes with regard to their own professional field and critically dealing with them;  
  • formulating a personal view about religions and world views in dialogue with the Christian faith in an argumentative manner:  
  • being capable to formulate the value of religion and world views for their own life;  
  • integrating religious and ethical dimensions in the development of their own professional identity

KU Leuven Vision on Education and Learning

Previous knowledge

This course does not require specific prior knowledge. General knowledge of the main lines of philosophy, ethics and western culture and history do belong to the presupposed background of the course. Concerning motivation, the students are not expected to be religious, but they are expected to be willing to reflect in an open and critical-scientific manner on fundamental ethical questions, and questions on the meaning of life, from different philosophical points of view, in particular, but not exclusively, the Jewish and Christian point of view.  

Identical courses

A08C4A: Religie, zingeving en levensbeschouwing
A04D5A: Religie, zingeving en levensbeschouwing

Is included in these courses of study

Onderwijsleeractiviteiten

Religions (B-KUL-H0N82a)

3 ECTS : Lecture 20 Second termSecond term

Content

Content Key themes in religion and theology are presented, based on insights of modern social sciences and contemporary philosophical thinking. The following questions are studied: what kind of purposes do religions serve, what is the core of the Christian faith and how can this be situated in the framework of other world religions? Both the relationship between Christianity and culture and Faith and Science is given much attention, as well as some classical themes which have always been the pivoting points of the Christian faith: the contribution of faith to personal happiness, the (Christian) expectations of a future life. Finally, the relevance of religious viewpoints on themes in engineering ethics will be presented.

Course material

Study cost: 11-25 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

The professor makes course notes available.

Format: more information

Interactive college. Apart from the lectures, a guest lecture could be organized.

Evaluatieactiviteiten

Evaluation: Religions (B-KUL-H2N82a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Multiple choice
Learning material : None

Information about retaking exams

Contrary to the first examination (multiple choice), re-examination consists of 3 open questions. 

ECTS Capita Selecta Mathematical Engineering I.2. (B-KUL-H0O30A)

3 ECTS English 20 Second termSecond term Cannot be taken as part of an examination contract

Aims

The aim and the content of the course are decided by the Educational Committee Mathematical Engineeering every academic year. The topic can be related to new actual themes in mathematical engineering that have (not yet) been adopted by a course, or the course can be used to help students improve prerequisites.

Previous knowledge

Depends on the actual content of the course.

Onderwijsleeractiviteiten

Capita Selecta Mathematical Engineering I.2. (B-KUL-H0O30a)

3 ECTS : Assignment 20 Second termSecond term

Content

The content is decided by the programme director and the educational committee every year, and is communicated to the students for whom the content is relevant. The content and working methods are shown in Toledo.

Course material

papers, literature

Format: more information

Depending on the content of the course: college or practical sessions.

Evaluatieactiviteiten

Evaluation: Capita Selecta Mathematical Engineering I.2. (B-KUL-H2O30a)

Type : Partial or continuous assessment with (final) exam during the examination period
Type of questions : Open questions
Learning material : Computer

Explanation

The students hand in (progress) reports of their work. This can be a report or software, depending on the topic of the course of that year. There is a final discussion during the examination period in June.

ECTS Capita Selecta Mathematical Engineering II.1. (B-KUL-H0O98A)

3 ECTS English 20 First termFirst term Cannot be taken as part of an examination contract

Onderwijsleeractiviteiten

Capita Selecta Mathematical Engineering II.1. (B-KUL-H0O98a)

3 ECTS 20 First termFirst term

Evaluatieactiviteiten

Evaluation: Capita Selecta Mathematical Engineering II.1. (B-KUL-H2O98a)

ECTS Capita Selecta Mathematical Engineering II.2. (B-KUL-H0O99A)

3 ECTS English 20 Second termSecond term Cannot be taken as part of an examination contract

Onderwijsleeractiviteiten

Capita Selecta Mathematical Engineering II.2. (B-KUL-H0O99a)

3 ECTS 20 Second termSecond term

Evaluatieactiviteiten

Evaluation: Capita Selecta Mathematical Engineering II.2. (B-KUL-H2O99a)

ECTS Complex Analysis and Applications (B-KUL-H0R80A)

4 ECTS English 45 First termFirst term
N. |  Mohammadi Fatemeh (substitute)

Aims

The student has obtained sufficient knowledge and insight into properties of complex functions and techniques from complex analysis to apply these to various engineering problems.
- The student can solve boundary value problems (with an emphasis on the problems described by partial differential equations) via conformal transformations, Laplace, Fourier (cosine/sine) transformation.
- Supported by complex analysis, the student knows properties and applications of polynomial approximations.
- The student has developed the attitude to learn the possibilities and limitations of approach and solution techniques, through numerical experiments.

Previous knowledge

Skills: the student should be able to analyze, synthesize and interpret. Knowledge: principles of analysis and linear algebra.

Identical courses

H09W6B: Complexe functieleer en toepassingen

Onderwijsleeractiviteiten

Complex Analysis and Applications: Lectures (B-KUL-H0R80a)

3 ECTS : Lecture 30 First termFirst term
N. |  Mohammadi Fatemeh (substitute)

Content

Analytic functions, elementary functions, Cauchy integral formula
- Taylor and Laurent series
- Contour integration and applications (calculation of improper integrals)
- Laplace, Fourier, Fourier sine/cosine transformation, applications in ordinary and partial differential equations
- Conformal transformations applied to partial differential equations (2D field calculations)
- Rational functions and applications

Course material

Syllabus; a few chapters from a textbook; Toledo

Complex Analysis and Applications: Exercises and Laboratory Sessions (B-KUL-H0R81a)

1 ECTS : Practical 15 First termFirst term
N. |  Mohammadi Fatemeh (substitute)

Course material

The assignments of the practice sessions and homework assignments are made available viaToledo.

Evaluatieactiviteiten

Evaluation: Complex Analysis and Applications (B-KUL-H2R80a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions
Learning material : Course material, Calculator

ECTS Entrepreneurship in de praktijk / in practice (B-KUL-H0T39A)

3 studiepunten Nederlands 60 Tweede semesterTweede semester Uitgesloten voor examencontract

Doelstellingen

Het doel van dit project is het opdoen van relevante ervaring rond ondernemerschap. Zo verwerft de student een beter inzicht in de praktische aspecten van het ondernemen.

Bij het voltooien van dit opleidingsonderdeel:

  • Kan de student ondernemerschap in praktische situaties toepassen.
  • Kan de student ondernemend handelen, door een idee om te zetten in de praktijk.
  • Is de student gegroeid in een aantal vaardigheden, zoals creativiteit tonen, innoveren, risico’s nemen, het plannen en organiseren van taken zodat de deliverables tijdig gerealiseerd worden, …
  • Kan de student over de uitgevoerde taken schriftelijk en mondeling verslag uitbrengen.
  • Kan de student reflecteren over zijn eigen functioneren binnen een project.

Begintermen

De student gaat zelfstandig op zoek naar een mogelijk project (bijvoorbeeld AFC, AFD, bij LCIE of deelname aan een ondernemingswedstrijd). Dit kan zowel binnen de non-profit sector als binnen de private sector.

De student dient een projectaanvraag in. Na goedkeuring kan de student dit opleidingsonderdeel in het ISP opnemen. Voor meer informatie: zie de website https://eng.kuleuven.be/studeren/engineering-essentials/stages/entrepreneurship-in-de-praktijk

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Entrepreneurship in de praktijk / in practice (B-KUL-H0T39a)

3 studiepunten : Opdracht 60 Tweede semesterTweede semester

Inhoud

De student verwerft ervaring over diverse facetten van ondernemerschap en ontwikkelt managementvaardigheden via participatie aan advies- en implementatieprojecten.

 

Het project moet verband houden met de opleiding tot ingenieur en voor aanvang inhoudelijk worden goedgekeurd door de coördinator van het OPO.

 

Voor de praktische regeling gelden de volgende richtlijnen:

  • De student zoekt zelf een project.
  • De student zorgt voor een correcte afhandeling van de nodige documenten, zoals een projectaanvraag, een tussentijdse rapportering, een contract indien nodig, …

Studiemateriaal

Geen

Toelichting werkvorm

Uitvoeren van creatieve en kwaliteitsvolle projecten voor een start-up, vzw, KMO, NGO, … Deze projecten hebben een duurtijd van één semester tot één jaar en kunnen, afhankelijk van het project, individueel of in teams worden uitgewerkt.

Evaluatieactiviteiten

Evaluatie: Entrepreneurship in de praktijk / in practice (B-KUL-H2T39a)

Type : Permanente evaluatie zonder examen tijdens de examenperiode
Evaluatievorm : Verslag, Presentatie

Toelichting

De evaluatie gebeurt aan de hand van een schriftelijke en mondelinge rapportering in overeenstemming met volgende richtlijnen.

Het verslag telt 10 tot 15 bladzijden en bestaat uit vier delen:

  • Deel A: situeert het project en bevat de administratieve gegevens: naam student, opleiding van student (inclusief fase en optie), naam project, periode, naam en contactgegevens van eventuele academische begeleider/projectleider (o.a. e-mailadres en telefoonnummer).
  • Deel B: omschrijft het project (de opdracht, het verloop en de behaalde resultaten). De student geeft telkens aan wat zijn taak precies geweest is.
  • Deel C: het reflectiegedeelte over de ervaring van de student. Deze bevat onder andere:
    • Kritische reflectie over de competenties die de student verwachtte te verbeteren (voeg die lijst als bijlage toe aan het verslag).
    • Relatie project en opleiding. Welke inhoud van welke opleidingsonderdelen is aan bod gekomen tijdens het project? Was die inhoud aangepast aan wat er nodig was?
  • Deel D: conclusies die uit het project getrokken werden. Zijn de doelen van het project bereikt? Was het project een meerwaarde voor de student?

De student dient dit verslag minstens een week voor de presentatie in.

Opmerking: indien de student herhaaldelijk of op ernstige wijze de verplichtingen vastgelegd in de projectaanvraag niet nakomt, kan de deelname aan het project worden stopgezet en wordt de eindbeoordeling voor het opleidingsonderdeel NA (niet afgelegd).

Toelichting bij herkansen

Indien het project als onvoldoende wordt beoordeeld, zal de student de verslaggeving moeten uitbreiden/verbeteren voor een evaluatie in de derde examenperiode. Het project zelf kan niet hernomen worden.

ECTS Project Mathematical Engineering (B-KUL-H0T46A)

3 ECTS English 12 Second termSecond term Cannot be taken as part of an examination contract
Nuyens Dirk (coordinator) |  Diehl Martin |  Feppon Florian |  Nuyens Dirk |  N. |  Goncalves Pedro (substitute)  |  Less More

Aims

The student can analyse a mathematical problem and solve it with software. The student can combine knowledge and skills acquired from different courses. The student can split up a practical problem in subproblems and present an acceptable solution to each subproblem and the full problem that satifies the relevant criteria. The student can present and demonstrate the methodology, solution method and results.

Previous knowledge

Design of scientific software, solution of ordinary and partial differential equations, optimization and complex analysis.

Order of Enrolment



SIMULTANEOUS(H0M86B) AND SIMULTANEOUS(H03E3A)


H0M86BH0M86B : Scientific Software
H03E3AH03E3A : Optimization

Identical courses

H0T44A: Project Wiskundige Ingenieurstechnieken

Is included in these courses of study

Onderwijsleeractiviteiten

Project Mathematical Engineering (B-KUL-H0T46a)

3 ECTS : Assignment 12 Second termSecond term
Diehl Martin |  Feppon Florian |  Nuyens Dirk |  N. |  Goncalves Pedro (substitute)

Content

The students work alone or in groups of two on a practical problem, related to an application arising from engineering. The topic is chosen in dialogue with the didactic team. The solution of the problem will make use of different courses. Students will have to find an elegant solution to the problem in a creative way, with insight in the technical matter. Weekly discussions with the didactic team are planned to help the students. The end product is software solving the problem.

The course planning is as follows. The activities take place in the first seven weeks of the second semester. In the first session, the topic is fixed for each group and a planning of the work is made. In the last session (week seven), the end result is presented; this includes a demonstration of the software and a technical presentation of the used methodology. In the intermediate weeks, the progress of the work is discussed with the didactic team and plans are possibly corrected.

Evaluatieactiviteiten

Evaluation: Project Mathematical Engineering (B-KUL-H2T46a)

Type : Continuous assessment without exam during the examination period
Description of evaluation : Project/Product, Presentation, Process evaluation
Type of questions : Open questions
Learning material : Computer

Explanation

The evaluation activities consist of discussions with the didactic team during the seven weeks of the project. The technical quality and the development process will be evaluated and the end solution will be demonstrated to the other students and the didactic team.

The evaluation is based on the quality of the solution of the technical problem and the methodology to arrive at the solution. Evaluation critera are the efficiency, technical correctness, usuability and readability of the software; and the correct or adequate use of learning outcomes of  courses that are used to solve the problem.

Because of the permanent evaluation and the follow-up during the academic year the possibility of a second chance during the third exam period is excluded.

Information about retaking exams

 

ECTS Entrepreneurship in practice / service-learning (B-KUL-H0T91A)

6 studiepunten Nederlands 0 Tweede semesterTweede semester Uitgesloten voor examencontract
Pontikes Yiannis (coördinator) |  Pontikes Yiannis |  Ranga Adrian |  Van Hertem Dirk |  N.

Doelstellingen

Concrete leerdoelen

 

Het doel van dit project is het opdoen van een relevante ervaring rond (sociaal) ondernemerschap. Zo verwerft de student een beter inzicht in de praktische aspecten van het ondernemen.

 

Bij het voltooien van dit opleidingsonderdeel:

  • Kan de student ondernemerschap in praktische situaties toepassen.
  • Kan de student ondernemend handelen, door een idee om te zetten in de praktijk.
  • Is de student gegroeid in een aantal vaardigheden, zoals creativiteit tonen, innoveren, risico’s nemen, het plannen en organiseren van taken zodat de deliverables tijdig gerealiseerd worden, actief luisteren en inspelen op de noden van de betrokken actoren, …
  • Is de student in staat om in een multidisciplinair team te werken en te communiceren met mensen van andere disciplines over de eigen discipline.
  • Kan de student over de uitgevoerde taken schriftelijk en mondeling verslag uitbrengen.
  • Kan de student reflecteren over zijn eigen functioneren binnen een project, de sociaal-maatschappelijke dienstverlening en de rol van technologie in het streven naar een duurzamere en inclusievere samenleving.

 

Bredere vormingsdoelen

 

  • De student verwerft waarden als integriteit, eerlijkheid, beoordelingsvermogen en inlevingsvermogen, en leert deze waarden toepassen.
  • De student ontwikkelt een sociaal-maatschappelijk verantwoordelijkheidsgevoel.
  • De student wordt zich bewust van het eigen denkkader, door middel van concrete en authentieke ervaringen.

Begintermen

Elke masterstudent die bereid is om een ondernemingsproject op te nemen, kan een aanvraag indienen. Omdat er een verscheidenheid aan projecten bestaat, hanteren we volgende werkwijze. De student gaat zelfstandig op zoek naar een mogelijk project (bijvoorbeeld via PiP, AFD, Humasol of Cera Award). Vervolgens dient de student een projectaanvraag in. Na goedkeuring kan dit opleidingsonderdeel in het ISP opgenomen worden. Voor meer informatie: zie de website: https://eng.kuleuven.be/studeren/engineering-essentials/stages/entrepreneurship-in-de-praktijk.

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Entrepreneurship in practice / service-learning (B-KUL-H0T91a)

6 studiepunten : Opdracht 0 Tweede semesterTweede semester

Inhoud

De student verwerft ervaring over diverse aspecten van (sociaal) ondernemerschap en ontwikkelt managementvaardigheden via participatie aan advies- en implementatieprojecten.

 

Het project moet verband houden met de opleiding tot ingenieur en voor aanvang inhoudelijk worden goedgekeurd door de coördinator van het opleidingsonderdeel.

 

Voor de praktische regeling gelden de volgende richtlijnen:

  • De student zoekt zelf een project.
  • De student zorgt voor een correcte afhandeling van de nodige documenten, zoals een projectaanvraag, een tussentijdse rapportering, een contract indien nodig, …

Studiemateriaal

Praktijkervaringen

Toelichting werkvorm

Uitvoeren van creatieve en kwaliteitsvolle projecten voor een start-up, vzw, KMO, NGO, … Deze projecten hebben een duurtijd van één semester tot één jaar en kunnen, afhankelijk van het project, individueel of in teams uitgewerkt worden.

 

Studenten die deelnemen aan een project rond sociaal ondernemerschap/service-learning, worden gevraagd om ook aan enkele intervisiemomenten deel te nemen. Service-learning is een didactische aanpak waarbij studenten een concreet maatschappelijk engagement aangaan en deze ervaring door middel van reflectie koppelen aan academische leerinhouden en persoonlijke en maatschappelijke vormingsdoelen. Voor meer informatie, zie https://www.kuleuven.be/onderwijs/sl.

 

Academische component:

Tijdens een ondernemingsproject past de student (disciplinespecifieke) kennis uit zijn opleiding toe en staat hij o.a. stil bij de rol van technologie in het streven naar een duurzamere en inclusievere samenleving en de link tussen zijn opleiding en sociaal-maatschappelijke dienstverleningen.

 

Praktijkcomponent:

De student doet een relevante ervaring over (sociaal) ondernemerschap op, waarbij hij in een interdisciplinair team werkt. Dit kan via verschillende kanalen:

  • deelname aan een jaarproject van Academics for Development (AFD), een organisatie die studenten de mogelijkheid biedt om een sociale impact in het zuiden te hebben;
  • een jaarproject rond ontwikkelingssamenwerking via de organisatie Humasol rond de thema's hernieuwbare energie, water en duurzame technologie;
  • deelname aan andere sociaal-maatschappelijk geëngageerde projecten en social profit organisaties via Cera Award;
  • een jaarproject met PiP (Product Innovation Project) om een innoverend product te creëren;
  • eventueel via een andere partnerorganisatie, na grondig overleg met de coördinator van het opleidingsonderdeel.

 

Reflectiecomponenten:

De student wordt gevraagd om in een tussentijds en eindverslag o.a. te reflecteren over de relatie tussen het project en zijn opleiding, en de competenties die hij verwachtte te verbeteren (die werden in de aanvraag gevraagd). Doorheen het academiejaar wordt de student door een coach van de partnerorganisatie begeleid en bv. gevraagd om na te denken over de eigen rol en mogelijkheden binnen het project en welke acties hij in de toekomst inzake maatschappelijke en/of sociale problemen kan ondernemen.

Evaluatieactiviteiten

Evaluatie: Entrepreneurship in practice / service-learning (B-KUL-H2T91a)

Type : Permanente evaluatie zonder examen tijdens de examenperiode
Evaluatievorm : Ontwerp/Product, Verslag, Presentatie

Toelichting

De evaluatie gebeurt aan de hand van een schriftelijke en mondelinge rapportering in overeenstemming met volgende richtlijnen.

 

De student stelt een tussentijds- en eindverslag op.

 

Het tussentijds verslag bestaat uit een inhoudelijke/technische omschrijving van het project enerzijds en een reflectie over de competenties die de student verwachtte te verbeteren anderzijds (deze werden in de aanvraag gevraagd). Elk deel mag maximum uit één A4 bestaan en kan in puntjes geschreven worden.

 

Het eindverslag telt 10 tot 15 bladzijden. Een mogelijke indeling van het verslag is:

  • Deel A: situeert het project en bevat de administratieve gegevens: naam student, opleiding van student (inclusief fase en optie), naam project, periode, naam en contactgegevens van eventuele academische begeleider/projectleider (o.a. e-mailadres en telefoonnummer).
  • Deel B: omschrijft het project (de opdracht, het verloop en de behaalde resultaten). De student geeft telkens aan wat zijn taak precies geweest is.
  • Deel C: het reflectiegedeelte over de ervaring van de student. Deze bevat onder andere:

               - Kritische reflectie over de competenties die de student verwachtte te verbeteren (voeg die lijst als bijlage aan het verslag toe).

               - Relatie project en opleiding. Welke inhoud van welke opleidingsonderdelen is aan bod gekomen tijdens het project? Was die inhoud aangepast aan wat er nodig was?

               - Relatie project en maatschappij.

  • Deel D: conclusies die uit het project getrokken werden. Zijn de doelen van het project bereikt? Was het project een meerwaarde voor de student?

 

Het verslag moet minstens een week voor de presentatie ingediend worden. Het project dient als een wetenschappelijke tekst omschreven te worden. Het reflectiegedeelte (deel C) mag wel persoonlijk geschreven zijn.

 

De presentatie duurt 15 à 20 minuten. Nadien wordt er tijd voorzien voor vragen.

 

Wanneer verschillende studenten aan hetzelfde project gewerkt hebben, mag het verslag deels collectief geschreven worden. De studenten dienen wel duidelijk aan te geven wie voor welk deel verantwoordelijk was. De reflectie over o.a. de competenties die zij tijdens het project beoogden te verwerven moet individueel gebeuren. De presentatie mag ook samen gegeven worden, zolang iedereen een deel geeft. Er mag dan langer gepresenteerd worden (tot 30 minuten).

 

Opmerking: indien de student de gemaakte afspraken en verplichtingen niet op correcte wijze nakomt, kan de deelname aan het project stopgezet worden en wordt de eindbeoordeling voor het opleidingsonderdeel NA (niet afgelegd).

Toelichting bij herkansen

 

ECTS Finite Elements, Part 2 (B-KUL-H9X21A)

3 ECTS English 35 First termFirst term Cannot be taken as part of an examination contract

Aims

The finite element method is generally considered of as the most powerful numerical technique to solve continuum problems.
Whereas in the 1960s this method has been developed within structural mechanics, nowadays applications can be found in fluid mechanics, solid mechanics, quantum mechanics... as well as in the study of heat transport, electromagnetism, etc.

The student is offered a broad view on the finite element method. The course starts with the general methodology to convert (differential) equations that describe a certain physical system are converted into a system of algebraic equations by applying the final element method.

Most illustrative examples will be taken from:
• structural mechanics (two- and three dimensional elastic continua, plates and shells, truss and  beams elements);
• heat transport: the differential equation ('quasi-harmonic equation') that describes heat transport has a form that is typical for a number of other disciplines, provided that temperature and derived quantities are translated properly;
• fluid mechanics.

The treatment of the finite element method has two main objectives:
• offer users the necessary theoretical background of finite element programs to be able to use them in a responsible and efficient manner for real practical problems. Therefore, attention will also be paid to aspects such as modelling, interpretation of results...
• offer potential developers of own finite elements programs (for new applications) the necessary basic knowledge.

Previous knowledge

The student should have command of a basic knowledge on continuum mechanics, differential and integral calculation and linear algebra. Preliminaries: elasticity and plasticity theory.

Onderwijsleeractiviteiten

Finite Elements, Part 2: Lectures (B-KUL-H04M3a)

2 ECTS : Lecture 22 First termFirst term

Content

Finite element course, part II: in this second part, the FEM is expanded to two and three dimensional continuum problems.
The following application domains are addressed: heat transport, solid mechanics and fluid mechanics. For each of these applications, the element equations are derived in detail.
Afterwards, element families are introduced (1D, 2D, 3D / linear, quadratic, cubic / straight and curved element boundaries...). For a number of element types, the intra-element variation is given of temperatures, heat fluxes / displacements, strains, stresses / velocities, pressures.
After an in depth treatment of the theoretical background, more application related topics are presented, e.g. symmetry, choice of element type, mesh density, automatic generation of elements, allowable deviations of the 'normal' element form, loads and boundary conditions, error estimators, interpretation of the results, quality of commercial FE packages.

Course material

Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)

  • Course text
  • Cook R.D., Malkus D.S., Plesha M.E., Concepts and Applications of FiniteElement Analysis, John Wiley, 1989
  • Cook R.D., Finite Element Modeling for Stress Analysis, John Wiley, 1995

Is also included in other courses

H04M0B : Finite Elements

Finite Elements, Part 2: Exercises (B-KUL-H04M4a)

1 ECTS : Practical 13 First termFirst term

Content

Through small manual calculations, insight into the background of this numerical analysis method (FEM) will be sharpened.
The students become familiarized with the most used elements, as present in the element libraries of current finite element programs and learn how to use them.
Finally, the students analyze a real case, in which they extensively check and interpret the results.

Course material

  • Book with exercises published by the Department of Civil Engineering
  • Interactive exercises offered on Toledo

  • Exercises from the recommended literature:
  • Cook R.D., Malkus D.S., Plesha M.E. Concepts and Applications of Finite Element Analysis, John Wiley, 1989
  • Cook R.D., Finite Element Modeling for Stress Analysis, John Wiley, 1995
  • Moaveni S. Finite Element Analysis: Theory and Applications with Ansys, Prentice-Hall, 1999

Format: more information

  • Making exercises
  • Interactive computer sessions
  • Applying the theory to a real-life case
  • Team work

Is also included in other courses

H04M0B : Finite Elements

Evaluatieactiviteiten

Evaluation: Finite Elements, Part 2 (B-KUL-H2X21a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Report
Type of questions : Open questions
Learning material : Calculator, List of formulas

Explanation

  • Assessment of the report of the case study (to be handed in after the exercise sessions).
  • Exam during the examination period.  A list of formulas can be used.

ECTS Transport Phenomena in Bioscience Engineering (B-KUL-I0W36A)

5 ECTS English 58 Second termSecond term Cannot be taken as part of an examination contract

Aims

Transport phenomena are important in numerous biological processes. This course introduces the basic principles of transport of heat, mass and momentum in bioscience engineering.  The emphasis is on the underlying physics, but also on the ability to distinguish between essential and less important aspects from an engineering point of view. The concepts are illustrated by examples from agriculture, biology, chemistry, food technology, environmental sciences and medicine.

After successfully concluding this course, the students will have the following competences:

  • Understanding the mechanisms of heat, mass and momentum transport
  • Constructing heat, mass and energy balances and solving the corresponding equations
  • Applying of these balances to typical transport problems a bioscience engineer will encounter

Previous knowledge

Attention for problem-solving reasoning on the basis of quantitative thinking and modeling.

Order of Enrolment



SIMULTANEOUS( I0N19B ) OR SIMULTANEOUS( G0N84B ) OR SIMULTANEOUS( G0O17D ) OR SIMULTANEOUS( X0C11A ) OR SIMULTANEOUS( X0E49A )


I0N19BI0N19B : Differentiaalvergelijkingen
G0N84BG0N84B : Differentiaalvergelijkingen
G0O17DG0O17D : Wiskunde II
X0C11AX0C11A : Differentiaalvergelijkingen deel I: gewone differentiaalvergelijkingen
X0E49AX0E49A : Differentiaalvergelijkingen deel I: gewone differentiaalvergelijkingen


Identical courses

I0N24A: Fysische transportverschijnselen
X0C04A: Fysische transportverschijnselen
X0E56A: Fysische transportverschijnselen

Is included in these courses of study

Onderwijsleeractiviteiten

Transport Phenomena in Bioscience Engineering: Lectures (B-KUL-I0W36a)

4 ECTS : Lecture 26 Second termSecond term

Content

Heat transfer

1. Equilibrium, energy conservation, temperature
2. Models of heat transfer
3. Governing equation and boundary conditions of heat transfer
4. Conduction heat transfer: steady-state
5. Conduction heat transfer: unsteady-state
6. Convection heat transfer
7. Heat transfer with phase change
8. Radiative energy transfer

 Momentum transfer

1. Pressure
2. Laminar flow
3. Turbulent flow
4. Mechanical energy balance: Bernoulli equation
5. Pumps

Mass transfer

1. Equilibrium, mass conservation and kinetics
2. Models of mass transfer
3. Governing equations and boundary conditions of mass transfer
4. Diffusion mass transfer: steady state
5. Diffusion mass transfer: unsteady state
6. Convection mass transfer

Course material

See Toledo for slides and extra information
- Compulsory manual:  Biological and Bioenvironmental Heat and Mass Transfer (2002) Ashim K. Datta, Marcel Dekker, Inc., New York. ISBN 0-8247-0775-3

Format: more information

Lecture with demonstrations-experiments

Transport Phenomena in Bioscience Engineering: Exercices (B-KUL-I0W37a)

1 ECTS : Practical 32 Second termSecond term

Content

During the exercise sessions, the student becomes familiar with the lecture topics and learns to apply the theory to practical problems that a bioscience engineer typically will encounter.

Via PC exercises (Python), students learn how to solve simple transport problems numerically.

Course material

See Toledo for the exercises (assignment + solution)

Format: more information

Exercise sessions: the students solve exercises with pen and paper/computer under the supervision of teaching assistants.

Evaluatieactiviteiten

Evaluation: Transport Phenomena in Bioscience Engineering (B-KUL-I2W36a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Open questions
Learning material : List of formulas, Calculator

Explanation

The evaluation of the course consists of a written exam during the exam period. The written exam is a closed book exam and includes one theory question, one exercise and five small detail questions. Students may use a clean formula list and a calculator (but not a graphical calculator). Cell phones, smart watches and smart glasses are not allowed.

Information about retaking exams

Same as primary exam

ECTS Studium generale: mens- en wereldbeelden (B-KUL-W0AH4A)

4 studiepunten Nederlands 26 Tweede semesterTweede semester Uitgesloten voor examencontract
Van Puyvelde Peter (coördinator) |  Allacker Karen |  Ramon Delphine (plaatsvervanger) |  D'hooge Jan |  Samoy Ilse |  Tampère Chris |  Van Puyvelde Peter |  Vermeiren Florian  |  Minder Meer

Doelstellingen

Dit opleidingsonderdeel wil de student een multidisciplinaire algemene vorming bieden om als kritische intellectueel te kunnen functioneren in de samenleving. Als dusdanig draagt het bij tot een van de belangrijke vormingsdoelen die de KU Leuven naar voren schuift in haar Visie op onderwijs en leren.

Leerresultaten

- De student heeft inzicht in het statuut van wetenschappelijke kennis en in de variëteit aan wetenschappelijke methoden.
- De student kent de draagwijdte van het gebruik (en misbruik) van cijfers in wetenschappelijk onderzoek en heeft aandacht voor de meest voorkomende denkfouten, zoals het verschil tussen correlatie en causaliteit.
- De student kan disciplinaire kennis plaatsen in een interdisciplinair perspectief en in een breed cultuurhistorisch perspectief.
- De student heeft inzicht in een aantal concrete maatschappelijke vraagstukken en kan ze benaderen vanuit verschillende perspectieven; op basis daarvan kan de student een gefundeerd standpunt innemen, rekening houdend met waarden en maatschappelijke impact.

Begintermen

Studenten hebben basiskennis binnen hun eigen discipline.

Plaats in het onderwijsaanbod

Onderwijsleeractiviteiten

Studium generale: mens- en wereldbeelden (B-KUL-W0AH4a)

4 studiepunten : College 26 Tweede semesterTweede semester

Inhoud

Studenten volgen de algemene module (4 sessies) en kiezen uit het aanbod twee interdisciplinaire modules (4 sessies elk). Tijdens een eerste inleidende sessie krijgen de studenten de nodige informatie over de opbouw van het opleidingsonderdeel en de manier waarop het wordt geëvalueerd.

De algemene module is verplicht voor alle studenten en bevat een aantal belangrijke cultuurhistorische en methodologische inzichten in wetenschappelijke kennis en de diversiteit tussen disciplines, met daarnaast aandacht voor kwesties als statistische denkfouten, wetenschapsfraude, bias en perceptie.

Vervolgens kiest elke student 2 thematische modules. Elke module wordt verzorgd door een interdisciplinair team van 3-4 lesgevers. Naast uiteenzettingen wordt binnen elke module ook ruimte gemaakt voor onderlinge discussie tussen studenten van verschillende disciplines.

Voorbeelden van thematische modules die kunnen worden uitgewerkt:

  • Materie, tijd en (ontstaan van) leven
  • Vrijheid en determinisme in menselijk gedrag
  • Taal, communicatie en identiteit
  • Perspectieven op geschiedenis, tijd en ruimte
  • Genetica en biotechnologie
  • Milieu, ruimtegebruik en voedselproductie
  • Biodiversiteit en global change
  • Economische ontwikkeling, armoede en crisis
  • Multiculturalisme, natievorming en global justice
  • Ongelijkheid, emancipatie en diversiteit
  • Uitdagingen in de zorg
  • Het Europese project

 

Studiemateriaal

Cursustekst voor de algemene module

Teksten en Powerpoint presentaties voor de specifieke modules worden ter beschikking gesteld via Toledo

Toelichting werkvorm

Interactieve colleges

Evaluatieactiviteiten

Evaluatie: Studium generale: mens- en wereldbeelden (B-KUL-W2AH4a)

Type : Permanente evaluatie zonder examen tijdens de examenperiode
Evaluatievorm : Paper/Werkstuk, Medewerking tijdens contactmomenten

Toelichting

Wat verwachten we van de studenten om te kunnen slagen? 

(1) De studenten zijn verplicht aanwezig tijdens alle sessies van de algemene module en de twee bijzondere modules die hen worden toegewezen. De studenten verwittigen afwezigheid voor aanvang van de sessie aan de coördinator van Studium Generale en attesteren de reden van afwezigheid met een officieel bewijsstuk (bv. doktersattest van de dag van de sessie).

(2) De studenten participeren actief aan de vierde sessie van de twee bijzondere modules, die de vorm zal aannemen van een debat, gemodereerd door de coördinator van de bijzondere module. De studenten bereiden ook de hen toegewezen taak voor die het debat zal voeden. Het einddoel van de debatsessie is te komen tot een ‘standpuntnota’ waarin de studenten een paar bezorgdheden / acties blootleggen rond het thema van de bijzondere module die volgens hen door de academische overheid in overweging genomen moeten worden (via onderwijs, onderzoek en/of dienstverlening).  

Toelichting bij herkansen

 

ECTS Philosophy of Technology (B-KUL-W0EN7A)

4 ECTS English 26 First termFirst term Cannot be taken as part of an examination contract

Aims

The aim of this course is to make students familiar with the most important themes and questions in the domain of Philosophy of Technology, from both a continental and analytic perspective. More specifically, we will consider, among other things, the link between technology on the one hand and, for example, science, ethics, and politics on the other.

At the end of the course, students have a critical insight into these questions and themes. This means, among other things, that they can clearly distinguish the different positions, and can explain the arguments pro and contra these positions. In addition, students have a good understanding of the links between the concepts that take a central place in the domain of Philosophy of Technology.

Previous knowledge

No specific knowledge of Philosophy or Technology is required.

Is included in these courses of study

Onderwijsleeractiviteiten

Philosophy of Technology (B-KUL-W0EN7a)

4 ECTS : Lecture 26 First termFirst term

Content

The following questions will be discussed:

1. Is technology/AI morally neutral?

2. Is technology a social construction? Or is society determined by technology/AI?

3. Is technological enhancement a moral problem?

4. Is all science technoscience?

5. Ethics and AI: privacy, sustainability, bias, transparency, responsibility, safety, etc.

 

Course material

PowerPointpresentations and articles. Both will be available on Toledo.

 

Format: more information

The course can be divided into three parts: lectures, a paper, and discussions based upon texts.

Evaluatieactiviteiten

Evaluation: Philosophy of Technology (B-KUL-W2EN7a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Paper/Project, Participation during contact hours
Type of questions : Open questions

Explanation

The final grade is based upon a paper (20%), a written exam (70%), and participation in the discussion (10%). 

Students who do not participate in all parts of the course (exam, paper, discussion) will get a grade ‘NA’ for the course (you don't get a final grade).

Information about retaking exams

During the third examination period, students should and can only retake those parts of the evaluation (paper and/or exam) for which they got no score or an insufficient score.

Participation in the discussion cannot be retaken during the third exam period. Students who did not participate in this part of the evaluation will again get a grade ‘NA’ for the course.

Students who should retake the course in a next academic year should re-do the entire course, including all parts of the evaluation.