These are notes that I use to prepare for class. These are not intended to be official lecture notes. In particular, I don't proof-read them carefully. However, if you do find typos, please let me know and I will correct them. You are expected to come to class and take notes. Please do not ask me to put up the notes before lectures, I may not always be able to do this.

1. Introduction and the PAC Learning Framework
2. Concentration Inequalities
3. Rademacher Averages and VC Dimension
4. Binary Classification
5. AdaBoost
6. SVMs and RKHS
7. Regression
8. Multiclass Classification
9. Optimization
10. Stability
11. Neural Networks and BP, Function Approximation
12. Online Learning and Generalization
13. Reinforcement Learning