Required in stage
Optional in stage
Second term
Both terms
Not organised
This year
Next year
Alternating years
External
Prerequisites
Identical coursesMachine Learning (B-KUL-T44MLE)
English
First term
Cannot be taken as part of an examination contract
Vanrumste Bart (coordinator) | Vanrumste Bart Aims
The objectives of the course are:
The student understands the machine learning algorithms. This is achieved through lectures during the semester.
The student can apply machine learning algorithms to unseen datasets. This is achieved through programming in PYTHON.
The student can calculate the performance of machine learning algorithms and formulate a strategy to improve performance.
Previous knowledge
Basic knowledge of Algebra, Calculus, Statistics is required.
Basic PYTHON programming skills are also required.
Order of Enrolment
Mixed prerequisite:
You may only take this course if you comply with the prerequisites. Prerequisites can be strict or flexible, or can imply simultaneity. A degree level can be also be a prerequisite.
Explanation:
STRICT: You may only take this course if you have passed or applied tolerance for the courses for which this condition is set.
FLEXIBLE: You may only take this course if you have previously taken the courses for which this condition is set.
SIMULTANEOUS: You may only take this course if you also take the courses for which this condition is set (or have taken them previously).
DEGREE: You may only take this course if you have obtained this degree level.
(FLEXIBLE(T2STAT) OR FLEXIBLE(T2STAE) OR FLEXIBLE(T1AWS0)) AND (FLEXIBLE(T34DAS))
The codes of the course units mentioned above correspond to the following course descriptions:
T2STAT : Statistiek (No longer offered this academic year)
T2STAE : Statistics (No longer offered this academic year)
T1AWS0 : Aanvullingen uit de wiskunde (No longer offered this academic year)
T34DAS : Data Science (No longer offered this academic year)
Identical courses
This course is identical to the following courses:
T44MLN : Machine learning
Is included in these courses of study
Activities
2 ects. Machine Learning: Lectures (B-KUL-44hMLE)
Content
Content:
Regression
Classification: Logistic regression
Neural Networks
Bias and Variance
Decision Trees
Recommender systems
Reinforcement Learning
Course material
Slides on the digital learning platform.
Format: more information
Guest lecture - Traditional lecture
We invite guest lectures from the industry to present recent evolutions in their domain.
2 ects. Machine Learning: Lab Sessions (B-KUL-44pMLE)
Content
Multiple programming exercises are made on the topics addressed in the theory part. The programming is done in Python.
A project is worked out in teams of two using unseen datasets.
Course material
We use the programming exercises in Jupyter Notebook. We use the PYTHON programming language.
Format: more information
Computer session - Poster presentation
A Programming exercises
With each chapter, there is a programming exercise linked in Jupyter Notebook. The functions developed in these exercise sessions are used for the project.
B The project
The project consists of 4 parts :
*The proposal part: here the students select a dataset on which they would like to work. They write a proposal of what they are going to do. The TA coach them in this process.
*The milestone part: here the students show their progress to the TA. They write a milestone report. The TA coaches the students.
*Poster: the students present a poster to the other students. This is marked.
*Final report: students submit a full report building further on the proposal part and the milestone part. This is marked.
The project is carried out in pairs of students.
Evaluation
Evaluation: Machine Learning (B-KUL-T71999)
Explanation
The final mark of this course is calculated based on the published component marks with the following weighting factors:
OLA Lecture: 40%
OLA Lab Sessions: 60%
Realization of the published partial marks:
OLA Lecture: the points are on the theory exam during the exam period. This is a closed-book exam consisting of multiple-choice questions and open questions.
A form of guessing correction is applied to multiple-choice questions.
OLA Lab Sessions: the points are on the final report and poster of the project. The project is carried out by two students.
The only exception to this rule is described in the complementary regulation of the Faculty of Engineering Technology to Article 66 in the Regulations on Education and Examinations.
Absences:
In case of absence during the obligatory coaching sessions and the poster session, you must notify the education ombudsman on the same day. Furthermore, contact the professor as soon as possible and certainly within a week.
In case of absence during the examination period, you must notify the examination ombudsman on the same day.
Information about retaking exams
This course unit allows partial mark transfers in case of partial pass mark:
- 44hMLE - Machine Learning: Lectures (during and beyond academic year)
- 44pMLE - Machine Learning: Lab Sessions (during and beyond academic year)
The same as in the first exam period.