In this course, we Introduce the basics of Machine Learning from a statistical perspective. The focus of this course is on supervised learning, but other learning paradigms are also studied. The following 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 learning - 16. Reinforcement learning.
GENERAL COMPETENCIES
Introduce the basics of Machine Learning from a statistical perspective. The student has to be able to 1) understand machine learning techniques, 2) formally prove theoretical guarantees about machine learning, 3) implement these techniques in Python, 4) apply these techniques to benchmark and real-world problems, and 5) evaluate the performance of machine learning techniques.
• 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 will gain insight in the studied methodologies and be able to reason about model complexities and learning guarantees.
• 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 python will enable the student to write programs to solve problems. The exercise sessions and practical exam project will challenge students to solve research questions that consider both synthetic and real-world data.
• 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 practical exam project will challenge students to collaborate with their peers and communicate their results effectively.
