6 ECTS credits
150 h study time

Offer 2 with catalog number 1002080CNR for all students in the 1st semester at a (C) Bachelor - specialised level.

1st semester
Enrollment based on exam contract
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Enrollment Requirements
Registration for “Machine learning” is allowed if one is registered for or has succesfully accomplished “Artificial Intelligence”, depending on the study plan one is enrolled for. For Master students registration is conditional to the agreement of the examination committee.
Taught in
Faculty of Sciences and Bioengineering Sciences
Computer Science
Educational team
Ann Nowe (course titular)
Activities and contact hours

26 contact hours Lecture
26 contact hours Seminar, Exercises or Practicals
150 contact hours Independent or External Form of Study
Course Content

- Learning of concepts
- Bayesian learning
- Instance based learning
- Inductive logic programming (learning of rules)
- Evaluation of hypotheses: confidence, bias and variance
- Computational learning theory
- Reinforcement learning
- Clustering
- Transparent and Trustworthy Machine Learning

Course material
Digital course material (Required) : Information on the case study, Learning platform
Digital course material (Required) : Transparencies, Learning platform
Handbook (Required) : Machine Learning, T.M. Mitchell, McGraw Hill, 9780071154673, 2004
Additional info

- Students need to perform 1 case study, in which  students' apply, evaluate and compare multiple learning techniques on real data.
- The information will be available on the learning platform.

- This course is given in Dutch, but can on demand, and if agreed by the students be given in English
- Recommended handbooks : Machine Learning, T.M. Mitchell, Machine Learning Ethem Alpydin,
- Transparencies are available on the learning platform.

Learning Outcomes

General competencies

- Knowledge and insight
The student is acquainted with a range of basic learning algorithms.
The student is capable of applying evaluation techniques to estimate the performance of the obtained model and to calculate the performance of an algorithm given a concrete application context using computational learning theory.

- The use of knowledge and insight:
He is able to choose the appropriate techniques given a concrete learning problem, to apply them correctly and to evaluate the obtained results.

- Judgement ability
The student must be able to to devise and sustain arguments in favor or against some choice of learning technique for a given problem.

- Communication
He/she can motivate the chosen approach to specialist and non specialists.

- Skills
Students have obtained the skills to autonomously program, analyse, and apply learning techniques to a wide variety of problems.


The final grade is composed based on the following categories:
Written Exam determines 75% of the final mark.
PRAC Practical Assignment determines 25% of the final mark.

Within the Written Exam category, the following assignments need to be completed:

  • written exam with a relative weight of 1 which comprises 75% of the final mark.

    Note: written exam with one oral question

Within the PRAC Practical Assignment category, the following assignments need to be completed:

  • assignment with a relative weight of 1 which comprises 25% of the final mark.

    Note: case study

Additional info regarding evaluation

- The exam is written, open book, and includes one question which can be optionally presented orally.
- The case study is mandatory and determines 25% of the final score.

Academic context

This offer is part of the following study plans:
Bachelor of Computer Science: Default track (only offered in Dutch)
Bachelor of Mathematics and Data Science: Standaard traject (only offered in Dutch)