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