6 ECTS credits
150 h study time
Offer 1 with catalog number 4013489ENW for working students in the 2nd semester at a (E) Master - advanced level.
The following Machine Learning Topics will be addressed:
1. The Learning Problem - 2. Is Learning Feasible? - 3. The Linear Model - 4. Error and Noise - 5. Training versus Testing - 6. Theory of Generalization - 7. The Vapnik-Chervonenkis Dimension - 8. Bias-Variance Tradeoff - 9. Neural Networks - 10. Overfitting - 11. Regularization - 12. Validation - 13. Support Vector Machines - 14. Kernel Methods - 15. Bayesian Machine Learning - 16. Reinforcement learning sample complexity - 17. Causality
The course is based on Y. Abu-Mostafa, M. Magdon-Ismail and H.-T. Lin "Learning from Data". Additional information can be found in S. Shalev-Shwartz and S. Ben-David "Understanding Machine Learning".
Study material: Scientific papers, course slides and Canvas notes
Introduce the basics of Machine Learning from a statistical perspective. The student has to be able to 1) understand the basic ideas behind these techniques, 2) implement these techniques using the Python ecosystem 3) apply these techniques to simple problems, and 4) evaluate their performance.
• Knowledge and insight: After successful completion of the course the student should have insight into which problems can benefit from machine learning techniques and how to apply these techniques to the problem at hand. The student should also have insight in methodological issues involved.
• Use of knowledge and insight: The student should be able to apply machine learning techniques and to tune the parameters of the chosen algorithm. The use of the Python Ecosystem should enable the student to write small programs to solve problems.
• Judgement ability: The student should be able to judge the qualities of the different machine learning techniques and their results on the problem at hand.
• Communication: The student should be able to communicate with experts about machine learning problems. The student should also be able to report and to present the results of his or her experiments to both specialists and non-specialists.
The final grade is composed based on the following categories:
Written Exam determines 50% of the final mark.
Practical Exam determines 50% of the final mark.
Within the Written Exam category, the following assignments need to be completed:
Within the Practical Exam category, the following assignments need to be completed:
Written exam: 50% of the final grade.
Practical project: 50% of the final grade.
For the practical project, the students get 4 assignments that they can solve in groups of two. The final exam for this project consists of giving an oral presentation of the solutions to these assignments.
This offer is part of the following study plans:
Master of Applied Sciences and Engineering: Applied Computer Science: Standaard traject (only offered in Dutch)
Master of Applied Computer Science: Default track (only offered in Dutch)
Master in Applied Sciences and Engineering: Applied Computer Science: Standaard traject
Master in Applied Sciences and Engineering: Computer Science: Artificial Intelligence (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Multimedia (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Data Management and Analytics (only offered in Dutch)
Master of Applied Sciences and Engineering: Computer Science: Artificial Intelligence
Master of Applied Sciences and Engineering: Computer Science: Multimedia
Master of Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering
Master of Applied Sciences and Engineering: Computer Science: Data Management and Analytics
Master of Teaching in Science and Technology: computerwetenschappen (120 ECTS, Etterbeek) (only offered in Dutch)