Valérie Monbet

Machine Learning

See also Course material, LABS, Course projects

LECTURE NOTES

1. Introduction

2. Linear models (I)

3. Linear models (II)

4. Artificial Neural Networks

5. Deep Learning

6. “Data-driven” models

7. Ensemble models (I)

8. Ensemble models (II)

9. Dimension reduction methods

10. Unsupervized learning

11. Kernel Methods (I)

12. Kernel Methods (II)