1. Introduction to statistical learning

2. Linear classifier

3. Shrinkage estimator

4. Model assessment and selection

5. Basis expansion and Kernel methods

6. Model assessment

7. Ensemble

8. Midterm Exam

9 Function estimation on high dimensions

10. Support Vector Machine (SVM)

11. Empirical risk minimization

12. Kernel machines

13. Deep learning: Introduction

14. Deep learning: Theories

15. Final Exam