At the end of the course, the student should be able to demonstrate an understanding of the different topics covered, i.e. be able to express in his/her own words the theory and methods seen in the course and explain in which context they are useful.
Machine learning is about computer programs that automatically improve their performance through experience (for example, programs that learn to recognise human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives.
Oral examination with preparation time.
References are given during the course.
| Training | Study programme | Block | Credits | Mandatory |
|---|---|---|---|---|
| Master in Computer Science (shift schedule) | Standard | 0 | 3 | |
| Master in Computer Science (shift schedule) | Standard | 1 | 3 |