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).

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.

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.

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.

Content

The goal of robust statistics is to develop and study techniques for data analysis that are resistant to outlying observations, and are also able to detect these outliers.

In this course we introduce notions of robustness such as the breakdown value and the influence function. We study several robust estimators of univariate location and scale, multivariate location and covariance, linear regression, and principal component analysis.

Course material

Course text and slides.

Format: more information

The course consists of lectures and some computer sessions in which the methods will be applied to real data.

Content

In computer sessions, robust methods will be applied to real data sets and the results will be interpreted. Some properties of the estimators will be verified empirically, for instance by Monte Carlo simulation.

Format: more information

The exercises will take place in computer classes.

Content

The students make a project in which they study the behavior and/or performance of certain robust methods on real and simulated data sets.

Explanation

The evaluation consists of a project and an examination. The specific form of the exam and the grading will be explained in class and on Toledo.

Information about retaking exams

If you received a passing mark for the project, this score can be retained.

Content

This course discusses the design of factorial experiments. Initially, the focus is on completely randomised experimental designs. Next, the focus shifts to experimental designs involving a restricted randomisation. First, the concept of blocking is discussed. Next, split-plot and strip-plot designs are studied.

The emphasis in the course is on the optimal design of experiments. In optimal design of experiments, the experimental design is tailored to the problem at hand (unlike classical experimental design, where inflexible, standard designs are chosen from catalogs). The course builds on concepts from regression and analysis of variance, such as fixed and random effects, power calculations, variance inflation factors, multicollinearity, confidence intervals, prediction and lack-of-fit tests.

Every topic in the course is introduced and illustrated by means of a case study from industry. The case studies are realistic in the sense that they involve quantitative and qualitative experimental factors, experimenters have to deal with limited budgets and difficulties to randomise, and forbidden combinations of factor levels. In each of the case studies, the goal is to enhance to performance of a process or a product. The areas of application are the food industry, the pharmaceutical sector, and the metal and chemical industries, among others.

The statistical software package used is JMP.

Course material

The textbook used is "Optimal design of experiments: A case study approach", co-authored by Peter Goos and Bradley Jones. A hard-copy of the textbook can be brought to the exam (photocopies, prints and e-books are not allowed at the exam).

Format: more information

The students are expected to master the concepts and understand the properties of and the differences between the different optimal experimental designs by studying the material from the lectures and the textbook. By means of an assignment and two PC sessions, the students get the opportunity to familiarise themselves with the JMP software required to plan experiments optimally.The assignment also requires the students to conduct a virtual experiment, so they can put the acquired knowledge into practice.

Is also included in other courses

G0B68B : Experimental Design

Explanation

The exam in June counts for 15 of the 20 points for the course. The assignment counts for 5 of the 20 points in June. A hard copy of the book "Optimal Design of Experiments: A Case Study Approach" can be brought to the exam (photocopies and prints of e-versions of the book are not allowed).

The details and consequences for students who do not submit the assignment (or do not submit the assignment in time) can be found on Toledo.

Information about retaking exams

The exam in August/September counts for 20 points. The score for the assignment is not carried over to August/September. A hard copy of the book "Optimal Design of Experiments: A Case Study Approach" can be brought to the exam (photocopies and prints of e-versions of the book are not allowed).

Content

The course focuses on major developments in physics during the first half of the twentieth century. The so-called "revolution in physics" is approached from different angles: the impact of new ideas and discoveries, the emergence of theoretical physics as a new important subdiscipline, the influence of political changes in Europe and World War II, the first debates on science policy and the funding of big science projects.

Themes to be discussed are:

-the 'slow' revolution of quantum physics and its relationship to "classical physics"
-quantum physics and the Weimar Zeitgeist
-physics, politics and warfare
-big science, big money
-the global development of physics

The course not only provide historical background, but also offers an introduction to some basic ideas of 'science studies'. In particular the social aspects of physics are being discussed, as well as some methdological issues in making historical and social analyses.

Course material

All study material is provided on the Toledo platform, and consists mainly of the presentation slides discussed during the course.
 

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

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

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.

Content

The learning activity for this part of the course consists of four main parts and a closing case study which integrates aspects discussed in the four parts. The four parts are:

Module I: Organizations and IT. This module explores the need for sound IT support for any organization in today’s world.Attention is paid to the potential benefits of IT, but also to the challenges. As such a realistic view on organizational expectations and realizations is achieved.

Module II: Organizational needs and IT strategy. This module continues on the link between organizational needs and IT strategy. The emphasis is on designing an IT system that supports the organization’s core activities in the best possible way.

Module III: Applications/Systems. This module consists of three parts.First part addresses e-business systems and gives an overview of the most common functional (e.g. human resources) and cross-functional (e.g. ERP, enterprise resource planning; CRM, customer relationship management; and SCM, supply chain management) IT systems. The second part focuses on e-commerce. The third part discusses both the organizational background of decision support systems as well as the different types of such systems

Module IV: Management issues. In this module attention is paid to ethical, social and political issues as well as to the management challenges of global systems.

Throughout the whole course aspects of technology, including trends in software & hardware and the impact on the organizational and managerial context are discussed. Another recurring aspects is IT project management, including best practices and pitfalls.

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.)

Reference book: Laudon, K.C and Laudon, J.O., Management Information Systems, 14th edition, Pearson, Harlow, 2016
Extra material (cases, papers, .... ) provided through TOLEDO

Format: more information

Interactive classes.  Case presentations and discussions. 

Content

Discussion of case studies or academic papers related to  course topics:. preparation in small groups, presentation of conclusions and class discussion for formulating a structured and clear answers.   Group presentation: students prepare a short presentation on an innovative technology or concept, after the presentation a class discussion is held.

D

Course material

Articles and case studies

Format: more information

Guest speakers from industry or service 
organizations are invited.

Content

Note that the order that the topics are covered in can vary from year to year.

Part I: Basics
1. Introduction and overview
2. Background on hashing, computer organization, complexity basics, etc.
3. Databases basics: SQL, join algorithms, index structures
4. Advanced topics: Fancy indexes, column store, warehouses, noSQL
 

Part II: Structures and techniques for efficiency
1. Introduction an overview
2. Learning from data streams
3. Fast nearest neighbors algorithms
4. Implementation tricks
5. Approximation methods (e.g., sketches, sampling)
6. Advanced topics?

Part III: Parallel Architectures
1. Introduction and overview
2. Types of parallelism (e.g., shared memory, shared nothing)
3. Concurrency
4. Parallel programming bugs (e.g., data races, deadlock, etc.)
5. Map-reduce
6. Cloud computing
7. Condor?

Course material

Lecture slides, readings, and online resources

Content

1. Part I: Query Languages
1. Introduction and overview
2. SQL: selection, projection, select-project-join, group-by, aggregates, subqueries, nested queries
3. Xquery
4. Sparql
5. Writing applications that can interface with a DBMS
6. Indexing?

2. Part II: Scripting Languages
1. Introduction an overview
2. Perl/Python

3. Part III: Parallel Architectures
1. Introduction and overview
2. Types of parallelism (e.g., shared memory, shared nothing)
3. Concurrency
4. Parallel programming bugs (e.g., data races, deadlock, etc.)
5. Map-reduce
6. Cloud computing
7. Condor?

Course material

Exercise slides

Content

Examples of the types of assignments
1. Given a set of verbal queries, translate them into a query language
2. Write queries to extract information needed for a machine learning task
3. Implement advanced machine learning algorithms (e.g., for learning from streaming data)
4. Implement an advanced data mining algorithm
5. A project that uses Hadoop, Spark, etc.

Course material

Assignment sheets

Explanation

The evaluation of the course will be based on multiple  programming assignments. Solutions are evaluated in terms of correctness, efficiency and generalizability.

Projects that are independent mean that students must complete the assignment individually. Thus using outside sources (e.g., publicly available code, etc.) or working together (e.g., working with somone else to solve the assignment, getting substantial help from someone else to solve the assignment, etc.) is strictly forbidden. If you questions about what is and is not permitted, please consult the instructor.

Information about retaking exams

For the project assignments with a failed result, the student will have an opportunity to complete an alternative assignment.

Content

1. Introduction
- Definition and general context, both of the domain and the course
 
2. State-space representation and search methods
- state-space representation: introduction and trade-offs,
- blind search,
- heuristic search, including the study of the A*-algorithm,
- advanced aspects of heuristic search,
3. Examples of search
- search in data mining: pattern mining
- heuristic search in games
4. Constraint propagation
- backtracking, backtrack variants, intelligent and dependency directed backtracking,
- arc consistency techniques,
- hybrid constraint propagation methods
 5. Some case studies of the use of constraint processing
- interpretation of line drawings,
- interpretation of natural language
 6. Planning and Temporal representation
- partial-order regression planning: STRIPS

Course material

Copies of the slides are made available by the student organisation VTK.

Is also included in other courses

H02A0C : Fundamentals of Artificial Intelligence

Content

1. Introduction (2 u.)
- Definition and general context, both of the domain and the course

2. State-space representation and search methods (8 u.)
- state-space representation: introduction and trade-offs,
- blind search,
- heuristic search, including the study of the A*-algorithm,
- advanced aspects of heuristic search,
- heuristic search in games
3. Examples of search: pattern mining and games (2 u.)
4. Constraint propagation (3 u.)
- backtracking, backtrack variants, intelligent and dependency directed backtracking,
- arc consistency techniques,
- hybrid constraint propagation methods
5. Some case studies of the use of constraint processing (3 u.)
- interpretation of line drawings,
- interpretatie of natural language
6. Planning and Temporal representation (1.5 u.)
- partial-order regression planning: STRIPS

Course material

Slides of the study material are available from the student organisation VTK

Is also included in other courses

H02A0C : Fundamentals of Artificial Intelligence

Content

The projects challenge the student to implement AI search techniques seen in class, in a Python programming environment based on the PacMan world developed at UC Berkeley. 

The project consists of two parts: 

• P1: Search: Students implement depth-first, breadth-first, uniform cost, and A* search algorithms. These algorithms are used to solve navigation and traveling salesman problems in the Pacman world. 
• P2: Multi-Agent Search: Classic Pacman is modeled as both an adversarial and a stochastic search problem. Students implement multiagent minimax and expectimax algorithms, as well as designing evaluation functions. 

You will fill in portions of provided Python files during the assignment. An autograder is provided to grade your own answers and guide you in your development; your submission will be evaluated by us 
independently. Collaboration and plagiarism are not allowed, we will check for this extensively and sanction if needed. 

Course material

The project assignment and the necessary software libraries and installation instructions will be made available on Toledo.

Explanation

Your final grade is determined by three parts: the project, the lecture exam and the exercise exam. The exact point distribution will be communicated through the online learning platform.

The project(s) will be evaluated during the year. They have to be made individually and plagiarism will be sanctioned. 

The exam is a written exam consisting of multiple-choice questions, fill-in questions and open questions. The exam consists of two parts: the part on the lecture material will test for factual and synthetic knowledge; the part on the exercises will test your ability to solve exercises involving the lecture material, as practiced in the exercise sessions. 

Content

Without being exhaustive, typical paradigms that could be presented are:

• constraint programming
• planning languages and systems
• theorem proving and systems for formal verification
• probabilistic reasoning and neuro-symbolic languages
• AI-extensions of logic, functional or object-oriented programming languages

For each introduced language, system or methodology, the important themes are the conceptual foundations, the main built-in representation and problem solving features, the illustrations of use and limitations, the indication of the key research aspects and the key applications.

The selection of studied paradigms can vary from year to year.

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.)

Slides and lecture material will be made available on the learning platform.

Content

The exercise sessions are related to the material of the course.

Course material

Exercises and necessary background material will be made available on the learning platform.

Explanation

Exam questions can contain a mix of open, closed and multiple choice questions. In case of multiple choice questions a guessing correction scheme will be communicated and used. The exact format of the exam will be announced on the learning platform.

Content

Robotics – theory lectures (3 ects) 

Lectures cover: 

• introduction to software development for robots 
• robot kinematics and dynamics 
• robot motion control and sensor-guided control, free-space and in-contact tasks 
• trajectory optimization (based on numerical optimization techniques) 
• dealing with uncertainty / estimation in robotics 
• classical motion planning and learning methods for motion planning 
• learning from demonstration 

Robotics – exercise and (virtual) laboratory sessions (1 ects) 

• (Computer) exercises and interactive lab visits on the lecture material

Course material

The study material consists of lecture notes, including paper references and book excerpts. 

Explanation

Students, in groups of two or, exceptionally, individually: 

• Do a homework assignment consisting of a set of computer programming exercises, and hand in a short report and their code. 
• Do a short research project. A set of possible projects will be made available by the lecturers. 

During the examination period there is an oral defence covering both the homework assignment and the research project. The homework assignment counts for 25% of the grade, the research project for 75%. 

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

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. 

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. 

Content

1. Speech Signal Processing and The Auditory Scene 

• the spectrogram, spectral estimation for acoustic signals
• elementary psychoacoustic concepts (loudness, pitch, timbre, critical bands)
• source-filter model of speech production, speech features (formants)
• mel spectrogram and cepstral features, spectral distance measures
• Dynamic Time Warping for matching signals that vary in time and frequency

2. Speech Recognition

• Classification of speech sounds and speech meta data
• Hidden Markov models, Viterbi decoding, training of  HMMs
• Decision Trees for Context-Dependent Phone modeling
• Linguistic resources: dictionaries, language models
• Large vocabulary decoding
• Static Deep Neural Nets for classifying speech sounds, Hybrid systems
• Time Warping Deep Neural Networks for Speech Recognition, a.o.:
  • Long Short-Term Model (LSTM), 
  • Connectionist Temporal Classification (CTC)
• Deep Neural Nets for language modeling
• Industrial outlook, applications of Speech Recognition

Course material

Course notes are available online

https://homes.esat.kuleuven.be/~compi/H02A6/course_notes/html

Additional material is available via Toledo

Content

There is a combination of lab sessions and exercises, all details are available in TOLEDO.  There are typically 6 sessions (though small deviations from the prototypical sequence are possible)

1. The Auditory Scene, Time - Frequency  representations of signals (speech & music)

2. Feature Extraction, Spectral Distance Metrics, Dynamic Time Warping

3. HMMs, Viterbi recognition

4. HMM training, context-dependent models, hybrid systems

5. DNNs for Language Modeling

6. Time Warping DNNs: CTC, LSTM 

Format: more information

All exercises are hand on sessions working out examples on pencil and paper, or - most of the time - working with Jupyter notebooks.

Students are expected to have enough computer skills to run Jupyter notebooks (Python), to understand the flow of the code by reading the - well documented - top level scripts and to be able to make minimal changes (such as changing variable assignments or skipping pieces of code).  Having some prior exposure to any scripting language is therefore deemed sufficient as a prerequisite.

Explanation

Open book exam with several exercises covering the different topics of the course. 

If, for organizational reasons (e.g. too many registered students), it is concluded that the organization of an oral exam is not feasible, then the oral exam will not take place and the written preparation will become a written exam. The impact of this decision will be explained on Toledo.

Example exams can be found here: http://homes.esat.kuleuven.be/~spchlab/H02A6/exams

Content

1. Introduction

• What is natural language processing (NLP)?
• Current state-of-the-art of NLP
• Ambiguity
• Other challenges
• Representing words, phrases and sentences
   

2. Segmentation and tokenization

• Regular expressions
• Word tokenization, lemmatization and stemming
• Sentence segmentation
• Subword tokenization
   

3. Language Modelling

• N-gram language models
• perplexity
• maximum likelihood estimation
• smoothing
   

4. Neural Language Modelling

• Word embeddings
• Vector space models for NLP
• Recurrent neural network (RNN) for language modelling
• Transformer architecture for language modelling
• Use of language models in downstream tasks: fine-tuning and pretraining
   

5. Part-of-Speech (POS) Tagging

• Hidden Markov model and viterbi
• Conditional Random Fields
• (Bi)LSTM for POS tagging
• Encoder-decoder architecture for sequence-to-sequence labeling
   

6. Morphological analysis

• Inflection and derivation
• Finite state morphology
• Sequence-to-sequence neural models of morphological inflection
   

7. Syntactic Parsing

• Universal Dependencies
• Dependency parsing: Graph based dependency parsing, transition based dependency parsing
• Constituent parsing with a (probabilistic) context free grammar ((P)CFG) and the Cocke-Younger-Kasami (CYK) algorithm
   

8. Semantics (lexical and compositional)

• Word sense disambiguation
• Semantic role labelling
   

9. Discourse: Coreference Resolution

• Discourse coherence
• Algorithm of Hobbs
• Neural end-to-end coreference resolution
   

10. Question Answering

• Evolution of QA systems from rule-based to neural 
• Complex pipelines to end-to-end to retrieval-free
• Closed-domain vs open-domain
• Text-only vs multimodal
   

11. Neural Machine Translation

• Encoder-decoder architecture (e.g., RNN, transformer-based)
• Attention models
• Improvements and alternative architectures that deal with limited parallel training data
   

12. Conversational Dialogue Systems and Chatbots

• Task oriented dialog agents: Rule based versus neural based approaches
• Chatbots: End-to-end sequence-to-sequence neural models
   

Course material

Handbooks

Daniel Jurafsky and James H. Martin. 2024. Speech and Language Processing: An Introduction to Natural
Language Processing, Computational Linguistics, and Speech Recognition with Language Models, 3rd edition.

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

Yoav Goldberg. 2016. A Primer on Neural Network Models for Natural Language Processing.

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

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

Format: more information

Interactive lectures with short exercises.

Is also included in other courses

G0D25A : Natural Language Processing

Content

• Exercises on tokenization and segmentation
• Exercises on language modelling and POS tagging
• Exercises on syntactic parsing
• Exercises on semantic and discourse processing
• Exercises on machine translation
• Exercises on question answering

Is also included in other courses

G0D25A : Natural Language Processing

Explanation

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

Content

• Introduction on the role of Knowledge Representation  in AI
  (Informal introduction to different types of knowledge and propositional attitudes, possible world analysis of knowledge, the role of knowledge in problem solving, the controversies and trade-offs of KR in AI)
•  Knowledge representation in classical logic
  (Syntax, informal and formal semantics of classical logic, KR methology in classical logic)
• Extending classical logic with definitions
  (introduction to different types of definitions and inductive definitions, syntax and formal semantics of ID-logic)
• Using classical logic for problem solving
  (The role of different forms of logical inference in AI and for problem solving)
•  Epistemic modal logic
  (Reasoning about knowledge, beliefs and intentions of other agents, syntax, formal and informal semantics of modal logic, correspondence theory, application to multi-agents systems)
• Knowledge representation in probabilistic logics
  (Introduction to probabilistic logics. Case study of CP-logic: syntax, informal and formal semantics.)
• Introduction to non-logical  KR-formalisms
  (Production rules and frame-based systems)

Course material

• Slides on Toledo
• Book "Knowledge Representation and Reasoning" Ronald Brachman and Hector Levesque

Content

• Introduction on the role of Knowledge Representation  in AI
  (Informal introduction to different types of knowledge and propositional attitudes, possible world analysis of knowledge, the role of knowledge in problem solving, the controversies and trade-offs of KR in AI)
•  Knowledge representation in classical logic
  (Syntax, informal and formal semantics of classical logic, KR methology in classical logic)
• Extending classical logic with definitions
  (introduction to different types of definitions and inductive definitions, syntax and formal semantics of ID-logic)
• Using classical logic for problem solving
  (The role of different forms of logical inference in AI and for problem solving)
•  Epistemic modal logic
  (Reasoning about knowledge, beliefs and intentions of other agents, syntax, formal and informal semantics of modal logic, correspondence theory, application to multi-agents systems)
• Knowledge representation in probabilistic logics
  (Introduction to probabilistic logics. Case study of CP-logic: syntax, informal and formal semantics.)
• Introduction to non-logical  KR-formalisms
  (Production rules and frame-based systems)

*

The excercise sessions address the following topics:

• KR methodology using classical logic
• Syntax , semantics and KR of definitional knowledge
• Use of inference tools for automated problem solving using declarative specifications
• Modal and multi-modal logics, Kripke structures
• Probabilistic logics, syntax and semantics of CP-logic

Course material

• Exercises and model solutions are made available on Toledo
• Students use the state-of-the-art inference system IDP to evaluate  the correctness of their solutions for KR excercises
• The system is also used to show the role of inference for automated problem solving using declarative specifications.

Explanation

Written exam (3h) :

•  Theoretical part: closed book
•  Exercise part:: open book  : only slides of the course
•  June and September
• No projects for this course

Content

Topics covered include (not necessarily in this order):

1) Data mining overview

2) The data mining process

3) Recommender systems

4) Association rule mining

5) Sequential pattern mining

6) Clustering

7) Large scale decision tree learning

8) Advanced topics on ensemble methods

9) Using unlabeled data

10) Data streams

11) Advanced topics (time permitting)

Content

The exercise sessions reinforce the central concepts covered during class and give students some experience working with publicly available data mining tools.  More specifically, tasks many include:

1) Working through the control of an algorithm to better understand how it functions

2) Implementing a small part of an algorithm

3) Working through a small part of the data mining process

4) Using Weka to analyse data

5) Theoretical questions designed to extend a student's knowledge of the subject

6) Discussing and solving a data mining problem with a small group and presenting the conclusions of the discussion to the whole exercise session

Explanation

Closed book written exam about the topics covered in the lectures, exercise sessions and reading.  The goal will be to assess two questions:

1) Do you understand the important basic concepts covered in class

2) Do you have an advanced understanding of the topics covered

Some questions will be similar in spirit to those solved in the exercise sessions while others will ask a student to apply a learned concept in a different context.  Be sure to read all the questions carefully and to think about how the answer to each question is structured.  

Content

The motivation for the course lies in the urgent need for computer programs that assist people in digesting masses of unstructured information composed of text and other media. We need information retrieval technology when, for instance, we find information on the World Wide Web, in repositories of news and blogs, in biomedical document bases, or in governmental and company archives. Moreover, emails, tweets, other messages and advertisements are searched and filtered. Various techniques of content recognition, recommendation and linking play an increasing role and allow generating content models of the documents or messages, that effectively match the personalized information needs of users. We witness a current interest in capturing dynamic changes in the data and in modeling dynamic interactions with users. The proliferation of wireless and mobile devices such as mobile phones has additionally created a demand for effective and robust techniques to index, retrieve and summarize information.

 
The lectures treat the following topics:
 

1. Introduction
 

 2. Advanced representations

 Law of Zipf

Matrix factorization, latent semantic analysis (LSA), training with singular value decomposition

Probabilistic latent semantic analysis (pLSA), latent Dirichlet allocation (LDA), training with Expectation Maximization (EM) algorithms, Markov chain Monte Carlo (MCMC) methods such as Gibbs sampling, and with variational inference

Embeddings obtained with neural networks

3. Retrieval and search models

Algebraic models: vector space models

Probabilistic models: language retrieval models and Bayesian networks

Neural network models

4. Learning to rank

Relevance feedback, personalized and contextualized information needs, user profiling

Pointwise, pairwise and listwise approaches

Structured output support vector machines, loss functions, most violated constraints

End-to-end neural network models

Optimization of retrieval effectiveness and of diversity of search results

5. Dynamic retrieval and recommendation

Static versus dynamic models

Markov decision processes

Multi-armed bandit models

Modelling sessions

Online advertising

6. Multimedia information retrieval

Multimedia data types and features

Concept detection

Cross-modal indexing of content: latent Dirichlet allocation and deep learning methods

Cross-modal and multimodal retrieval and recommendation models

Illustrations with spoken document, image, video and music search

7. Web search

Web search engines, crawler-indexer architecture, query processing

Link analysis retrieval models: PageRank, HITS, personalized PageRank and variants

Behavior and credibility based retrieval models

Social search, mining and searching user generated content

8. Scalability of Web search 

Data structures and search techniques

Inverted files, nextword indices, taxonomy indices, distributed indices

Compression

Learning of hashing functions, cross-modal hashing

Scalability and efficiency challenges

Architectural optimizations

9. Clustering

Distance and similarity functions in Euclidean and hyperbolic spaces, proximity functions

Sequential and hierarchical cluster algorithms, algorithms based on cost-function optimization, number of clusters

Term clustering for query expansion, document clustering, multiview clustering

10. Categorization

Feature selection, naive Bayes model, support vector machines, (approximate) k-nearest neighbor models

Deep learning methods

Multilabel and hierarchical categorization

Convolutional neural network (CNN) based hierarchical categorization

11. Summarization

Document segmentation, maximum marginal relevance

Summarization based on latent Dirichlet allocation models and long short-term memory (LSTM) networks

Abstractive summarization with attention models

Multidocument summarization, search results fusion and visualization 

12. Question answering and conversational agents in search and recommendation

Retrieval based question answering

Deep learning methods including attention models

Cross-modal question answering

E-commerce search and recommendation

13. Evaluation measures and methodology

Recall, precision, F-measure, mean average precision, discounted cumulative gain, mean reciprocal answer rank, accuracy, confusion matrix, ROC curve, normalized mutual information, mean absolute error, root mean square error, pyramid method, inter-annotator agreement, test collections

14. Discussion of interesting research projects

15. Invited lecture by representative of an important company

In 2006-2007: Thomas Hofmann, Director of Engineering, Google Zurich European Engineering Centre, Switzerland; in 2007-2008: Ronny Lempel, director of Yahoo! research, Israel; in 2008-2009: Stephen Robertson, senior researcher at Microsoft Research Cambridge, UK and one of the founders of probabilistic modeling in information retrieval; in 2009-2010: Gregory Grefenstette, Chief Science Officer, Exalead, France; in 2010-2011: Mounia Lalmas, visiting senior researcher at Yahoo! Labs Barcelona, Spain; in 2011-2012: Jakub Zavrel, CEO and founder of TextKernel, The Netherlands; in 2012-2013: Massimiliano Ciaramita, senior research scientist at Google, Zürich, Switzerland; in 2013-2014: Alex Graves, senior research scientist at Google DeepMind, London, UK; in 2014-2015: Fabrizio Silvestri, Senior Scientist at Yahoo Labs, Barcelona; in 2015-2016: Roi Blanco, Senior Scientist at Yahoo Labs, London; in 2016-2017: Holger Schwenk, research scientist at Facebook AI Research, France and Dani Yogatama, research scientist at Google DeepMind, London, UK; in 2017-2018: Enrique Alfonseca, research tech leader at Google AI, Zurich; in 2020-2021: Florian Strub, senior researcher at Google Deepmind, and in 2021-2022: Rylan Conway, applied scientist at Amazon Seattle.

Course material

Course material is available on the Toledo-platform of the K.U.Leuven. The following books offer background to the course material:
Baeza-Yates, R. & Ribeiro-Neto, B. (2011). Modern Information Retrieval: The Concepts and Technology behind Search (2nd edition). Harlow, UK: Pearson.
Büttcher, S., Clarke, C.L.A. & Cormack, G.V. (2010). Information Retrieval: Implementing and Evaluating Search Engines. Cambridge, MA: MIT Press. 
Manning, C.D., Raghaven, P. & Schütze, H. (2009). Introduction to Information Retrieval. Cambridge University Press.
Moens, M.-F. (2006). Information Extraction: Algorithms and Prospects in a Retrieval Context (International Series on Information Retrieval 21). Berlin: Springer.

 

Format: more information

Interactive lectures.

Is also included in other courses

H02C8A : Information Retrieval and Search Engines

Content

• Exercise session on latent semantic models, probabilistic and vector models
• Exercise session on learning to rank
• Exercise session on dynamic retrieval
• Exercise session on compression
• Exercise session on categorization and clustering
• Exercise session on link based and multimodal models

Course material

Exercises and answers are available via the Toledo platform. 

Format: more information

Interactive exercise sessions in small groups.

Is also included in other courses

H02C8A : Information Retrieval and Search Engines

Content

The assignment is a programming assignment on a given problem of current research interest.

Explanation

- An assignment (grading: 33.3%): At the start of the course (week 7) the student receives a programming assignment, which regards a specific problem in information retrieval, search or recommendation. The assignment is due during week 21. The score on the assignment is transferred to the third examination period.
- Theory exam (grading: 33.3 %): Written, open book.
- Exercise exam (grading: 33.3%): Written, open book.

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).

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

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.

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.

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.

Is also included in other courses

H02D3B : Support Vector Machines: Methods and Applications

Format: more information

3 computer exercise sessions

Is also included in other courses

H02D3B : Support Vector Machines: Methods and Applications

Explanation

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

Content

Introduction

• Agents and autonomy, agents vs. objects, agents vs. expert systems
• Cooperative vs competitive multi-agent systems
• Distributed vs centralised vs decentralised
• Task- vs state- vs worth-oriented domains
• Application domains
  • cyberphysical systems (CPS) - robotics, logistics, automated driving
  • agent-based modelling
• Agent, multi-agent systems and software engineering

Autonomous agents & agent architectures

• deliberative / theoretical reasoning agents
• reactive and behaviour-based agents, Brooks’ subsumption architecture, Agent network architecture
• practical reasoning agents - belief-desire-intention (BDI)
• horizontally and vertically layered architectures

Automated planning & acting

• Planning vs acting
  • descriptive vs operational models
• Planning: models, properties & algorithms
  • Deterministic / classical planning
  • Temporal planning
  • Non-deterministic planning & discretization
  • Probabilistic planning
  • Hierarchical task networks
• PDDL

Multi-agent planning

• POCL planning
• Parallel POCL planning
• Multi-agent POCL planning
• MA-PDDL

Multi-agent task allocation

• contract net + variants (incl dynamic, with confirmation)
• task trees and subtree bidding
• gradient fields
• agent negotiations for task re-distribution
  • deals, conflict deal, Zeuthen strategy

Swarm intelligence

• emergent behaviour, self-organization
• stigmergy
• ACO (ant colony optimisation) - TSP, AntNet, AntSystem

Delegate MAS for large-scale dynamic coordination and control applications

• task & resource agents
• exploration, intention and feasibility
• case study - manufacturing control & logistics

Competitive multi-agent systems & game theory

• non-cooperative game theory
• coalitional game theory

Course material

• An Introduction to MultiAgent Systems - Second Edition, M. Wooldridge, 2009.
• M. Ghallab, D. Nau, P. Traverso, “Automated planning and acting” - http://projects.laas.fr/planning/, 2016
• Various papers and book chapters from literature.

Content

A scientific project aims at experiencing the challenges as well as the opportunities that multi-agent systems entail in distributed problem solving. The project includes a limited literature study, practical development, evaluation, reporting.

Explanation

• Questions & exercises on the covered concepts and techniques for MAS-based modeling and problem solving
• Discussion of project work

Inhoud

De stage “Bedrijfservaring” kan enkel in de zomer plaatsvinden en telt mee als vak in het daaropvolgende academiejaar. Deze stage kan gedaan worden in de zomer na de bachelor of in de zomer na het eerste masterjaar.

De minimumduur is 4 weken.

De bedrijfservaring moet verband houden met de opleiding. Bedrijven uit alle mogelijke sectoren komen in aanmerking, zowel in binnenland als buitenland. Een bedrijfservaring in een onderzoekslabo van de Faculteit Ingenieurswetenschappen is niet mogelijk.

De student moet zich bij de stagecoördinator (Yolande Berbers) aanmelden. Meer informatie hierover staat op de volgende site. Deze aanmelding moet vóór 1 juni van het jaar waarin de stage plaatsvindt gebeuren.

Een lijst van bedrijven die stageplaatsen aanbieden kan gevonden worden op de volgende site. 
Studenten mogen ook zelf een stage plaats zoeken.

Ten laatste op 1 juli oploadt de student een document met daarin de competenties die de student hoopt te verbeteren tijdens de stage, Meer informatie hierover is te vinden op de bovenvermelde website. 

Studiemateriaal

/

Toelichting

De evaluatie gebeurt op basis van een verslag over de stage dat de student opmaakt na afloop van de stage en op basis van een presentatie.

Deadline voor indienen van het verslag: 15 oktober van het jaar waarin de stage plaats vond.

Richtlijnen voor stageverslag:
Het gevraagde verslag is een technisch verslag, geen reisverslag of vakantieopstel.
Op de volgende website vind je richtlijnen voor het schrijven van een technisch verslag:
https://eng.kuleuven.be/studeren/engineering-essentials/rapporteren/schriftelijk
Voor de lengte van het stageverslag telt het aantal woorden: Dit moet minimum 4000 woorden zijn, exclusief titelblad, inhoudstafel, abstract, administratieve gegevens, referenties en appendices.

Het stageverslag bevat de volgende elementen:

• Administratieve gegevens: naam student, studiejaar van student (inclusief optie), naam en adres van stagebedrijf, naam en contactgegevens (o.a. email-adres en telefoonnummer) van stagebegeleider, stageperiode
• Beknopte beschrijving van het bedrijf (geen cut en paste van de website), besteed ook aandacht aan de locale situatie. Beschrijf de plaats van dit bedrijf in het economisch landschap. Ga ook na wie de concurrenten zijn. (max 2 blz.)
• Omschrijving van het werk tijdens de stage. Zorg voor een degelijke structuur. Gebruik de juiste softwareterminologie. Besteed o.a. aandacht aan de softwaretechnologie die het bedrijf gebruikt, en aan de innovatie die het bedrijf al dan niet voert.
• Relatie stage en opleiding (minstens 1 blz., maximum 3 blz.): Welke inhoud van welke cursussen zijn aan bod gekomen tijdens de stage? Was die inhoud aangepast aan wat er nodig was? Wees kritisch!
• Kritische reflectie over de competenties die de student verwachtte te verbeteren (voeg die lijst toe als bijlage aan het verslag)
• Conclusies die uit de stage getrokken werden: Zijn de doelen van de stage bereikt? Was de stage een meerwaarde voor de student? 

Toelichting bij herkansen

Content

The course will cover the basics in management of cybersecurity posture of an organization. Illustrations follow in 6 invited talks by operational managers in industry and field experts. 

The lecture components covers the following topics: 

• Operational management to achieve security and privacy: overview. 
• Organizational aspects: processes, storage of critical security date, roles and responsibilities. 
• Outsourcing: impact, consequences, tendencies and best practices. 
• Application to specific security domains: Security Infrastructure, IAM, BYOD, Backup and business continuity,… (4 subdomains and related subsystems). 

Course material

• Slideware of the lectures, typically related to risk, compliance, governance, policy, and focusing on security operations: tools, practices and processes of threat prevention, detection and incident management in the specific security domains 
• Presentations of the industry case studies by guest lecturers. 

Is also included in other courses

H0O37A : CyberSecurity Management and Governance: Operations

Content

Operational security tool review exercises to be performed in pairs. The exercises are electronic using the e-learning platform, but also supported class with class sessions. (Open) discussion on tools and practices.

Course material

Operational security tool review exercises to be performed in pairs. The exercises are electronic using the e-learning platform, but also supported class with class sessions. (Open) discussion on tools and practices.

Explanation

Three theoretical/conceptual questions and one design-oriented question. 
The report on the exercises has a 20% weight and is mandatory. 

Content

Firstly, a general introduction will be given on the use of formal systems, their applications and on the typical structure and composition of a formal system. Afterwards, a number of concrete systems will be looked at in more detail. Which systems will be dealt with precisely will depend on each year. Possible systems include:
·         The lambda calculus and its application in the semantics of sequential programming languages;
·         The pi calculus and its application to the verification of protocols;
·         The spi calculus and its application to the specification and verification of the safety and security of distributed 
·         The ambient calculus and its application to the specification and verification of the safety of mobile code 
·         Formal type systems and their application in programming languages
·         Logics with module systems and graphical logics and their application to the specification of large systems

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.)

Content

First, a general introduction will be given on the use of formal systems, their applications and on the typical structure and composition of a formal system. Afterwards, a number of concrete systems will be looked at in more detail. Which systems will be dealt with precisely will depend on each year. Possible systems include:
·         The lambda calculus and its application in the semantics of sequential programming languages;
·         The pi calculus and its application to the verification of protocols;
·         The spi calculus and its application to the specification and verification of the safety and security of distributed 
·         The ambient calculus and its application to the specification and verification of the safety of mobile code 
·         Formal type systems and their application in programming languages
·         Logics with module systems and graphical logics and their application to the specification of large systems

*

Practical exercises with the lecture Formal Systems and their Applications. This can include the application of one of the formal systems studied to example problems, the design of a variant of an existing formal system, or the implementation of one of the formal systems studied in the course in order to perform some experiments.

Explanation

·         An implementation project, applying the ideas covered in lectures (25%).
·         A final exam.
 
The implementation project can not be redone for the september exam. The score on the project obtained in june is maintained for the september evaluation.

Content

• Physical & data link layer technologies:
  • Wired networks: e.g. current loop, Heart and RS435.
  • Wireless star networks: e.g. Bluetooth and RFID
  • Wireless mesh networks: e.g. 6TiSCH and Bluetooth Mesh
  • Low Power Wide Area Networks: e.g. LoRa, NB-IoT, LTE-M
• Network layer technologies:
  • Static routing: e.g. 6TiSCH, RPL
  • Ad-hoc routing: e.g. AODV
• Transport layer technologies: e.g. MQTT and CoAP
• Application layer technologies:
  • Middleware: Microsoft Azure, Amazon IoT, IFTT
  • Data storage: time series databases and historians.

Content

Exercises are mandatory and assessed focussed the development of a simple yet end-to-end industrial internet system from sensors and actuators to the cloud.

Course material

Lab booklet.

Explanation

Assessment during term time via programming exercises is taken into account for the final mark at the same ratio as the ECTS credits (4 credits for lectures/exam, 1 credit for assignments during term time).

For students that do not succeed in their assessment the first time, they are free to repeat the assessment in the weeks of the semester during the usual timetable.

Inhoud

De industriële stage kan enkel in de zomer plaatsvinden voor het tweede masterjaar en telt mee als vak in het daaropvolgende academiejaar.

De minimumduur is 6 weken.

De inhoud van de stage moet verband houden met de opleiding. De stageopdracht moet een afgerond geheel vormen, waar liefst zowel analyse van het probleem als het ontwerp van een oplossing en de implementatie daarvan aan bod komen. Bedrijven uit alle mogelijke sectoren komen in aanmerking, zowel in binnenland als buitenland. Een industriële stage in een onderzoekslabo van de Faculteit Ingenieurswetenschappen is niet mogelijk.

Namen en contactgegevens van bedrijven die stageplaatsen aanbieden kunnen gevonden worden op de volgende site. Een student kan ook zelf een stageplaats zoeken.

De student moet zich bij de stagecoördinator (Yolande Berbers) aanmelden vóór 1 juni van het jaar waarin de stage plaatsvindt gebeuren. Meer informatie staat op deze site.

De inhoud van de stage wordt beschreven in een stageplan. Dit stageplan is 2 à 3 blz. lang en moet heel concreet zijn. De student stelt het zelf op, in samenspraak met het bedrijf. Uiterlijk op 1 juli uploadt de student het stageplan. Het stageplan bevat de volgende elementen:

• probleemstelling
• concrete doelstelling (wat ga je bereiken)
• werkplan, verdeeld in meerdere taken, waarbij de taken al enigszins omschreven zijn (nl. hoe ga je de doelstellingen bereiken), liefst ook met een schatting van de grootte van de taken en een tijdsindeling (dat is natuurlijk maar een schatting).
• korte beschrijving van de plaats waar de stage zal doorgaan (fysische locatie, grootte van de groep waarbinnen men werkt, naam en contactgegevens van dagelijkse begeleider o.a. emailadres en telefoonnummer).

Ten laatste op 1 juli uploadt de student een document met de competenties die de student hoopt te verbeteren tijdens de stage, Meer informatie hierover staat op bovenvermelde website.

Studiemateriaal

/

Toelichting

De student moet een verslag maken en een mondelinge voorstelling geven over het verloop en de inhoud van de stage.

De evaluatie van dit OPO gebeurt op basis van dit verslag en van de voorstelling.

Richtlijnen voor stageverslag:
Het stageverslag is een technisch verslag, geen reisverhaal of een vakantieopstel.
Op de volgende website vind je richtlijnen voor het schrijven van een technisch verslag:
https://eng.kuleuven.be/studeren/engineering-essentials/rapporteren/schriftelijk
Voor de lengte van het stageverslag telt het aantal woorden: Dit moet minimum 8000 woorden zijn, exclusief titelblad, inhoudstafel, abstract, administratieve gegevens, referenties en appendices.

Het verslag bevat de volgende elementen:

• Administratieve gegevens: naam student, studiejaar van student (inclusief optie), naam en adres van stagebedrijf, naam en contactgegevens van stagebegeleider (o.a. emailadres en telefoonnummer), stageperiode
• Beknopte beschrijving van het bedrijf (geen cut en paste van de website), besteed ook aandacht aan de locale situatie. Beschrijf de plaats van dit bedrijf in het economisch landschap. Ga ook na wie de concurrenten zijn. (max 2 blz.)
• Omschrijving van het werk tijdens de stage. Dit vormt de hoofdmoot van het verslag. Zorg voor een degelijke structuur. Geef een gedetailleerde beschrijving van de deeltaken die uitgevoerd werden. Dit deel moet verwijzen naar het stageplan, en ook dit stageplan kritisch evalueren. Besteed aandacht aan de software technologie die het bedrijf gebruikt, en de innovatie die het bedrijf al dan niet voert.
• Alternatieve methodes / technologieën: Het bedrijf heeft voor zijn softwareontwikkeling bepaalde keuzes gemaakt (programmeertaal, platform,
  methodologie, softwarearchitectuur, enz.). Geef aan of voor jouw ontwikkeling je alternatieve methodes, technologieën, ... zou hebben kunnen 
  gebruiken. Wees kritisch!
• Zeg iets over de begeleiding van het bedrijf, de vorm van begeleiding, de frequentie, ...
• Relatie stage en opleiding (ca. 2 blz.): Welke inhoud van welke cursussen zijn aan bod gekomen tijdens de stage? Was die inhoud aangepast aan wat er nodig was? Wees kritisch!
• Kritische reflectie over de competenties die je verwachtte te verbeteren (voeg die lijst toe als bijlage aan het verslag). Wees hierin niet oppervlakkig!
• Conclusies die uit de stage getrokken werden. Zijn de doelen van de stage bereikt? Was het een meerwaarde voor de student?

Het verslag moet ingediend worden ten laatste op 15 oktober van het jaar waarin de stage plaats vond.

• Bijlagen: het stageplan en de lijst van competenties die je verwachtte te verbeteren.

Toelichting bij herkansen