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.
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
- 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
- The course is in English
- Course book: Machine Learning, T.M. Mitchell
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.
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:
Within the PRAC Practical Assignment category, the following assignments need to be completed:
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.
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)
Bachelor of Artificial Intelligence: Default track (only offered in Dutch)
Master of Electromechanical Engineering: Sustainable Transport and Automotive Engineering (only offered in Dutch)
Master of Electromechanical Engineering: Sustainable Transport and Automotive Engineering