6 ECTS credits
150 h study time

Offer 2 with catalog number 1024443CNR for all students in the 1st semester at a (C) Bachelor - specialised level.

Semester
1st semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Enrollment Requirements
Students must have followed ‘Introduction to Artificial Intelligence’, before they can enroll for ‘Machine learning’. The enrolment requirement is only valid for bachelor students.
Taught in
Dutch
Faculty
Faculty of Sciences and Bioengineering Sciences
Department
Computer Science
Educational team
Ann Nowe (course titular)
Activities and contact hours
26 contact hours Lecture
26 contact hours Seminar, Exercises or Practicals
Course Content

Learning of concepts (version spaces and decision trees)
Bayesian learning
Instance based learning
Neural Networks
Evaluation of hypotheses: confidence, bias and variance
Computational learning theory
Reinforcement learning
Clustering

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

- The student needs to perform a case study in which a machine learning approach is applied on real data.
- The information will be available via the learning platform
- Course book:  Machine Learning, T.M. Mitchell

 

 

Learning Outcomes

General competencies

Knowledge and insights:
The student has knowledge about a broad spectrum of learning techniques and insights in this research domain. The student has knowledge and insights about existing methods to evaluate obtained hypotheses. The student is capable to follow a specialised master-level course in this domain.

Application of knowledge and insights
The student is capable of making an informed choice regarding learning algorithms to solve new concrete problems and of applying these techniques correctly, as well as evaluating the obtained results.

Forming judgement
The student has to be capable to give sound arguments around applying given algorithms on given problem settings.

Communication
The student is capable to motivate and communicate choices towards both experts and  non-experts in the domain.

Learning skills
The student has developed the necessary skills to independently implement and analyse learning algorithms, and to apply these techniques on a large array of problems.

 

Grading

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

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 written examination represents 75% of the final grade.
The case study assignment is mandatory and represents 25% of the final grade. The student should obtain a final grade of at least 10/20, as well as score at least 7/20 for each of the parts composing the final grade (the case study and the written examination), in order to pass the course.

 

Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Bachelor of Computer Science: Default track (only offered in Dutch)
Bachelor of Artificial Intelligence: Default track (only offered in Dutch)