Lecture 1 (Introduction).
Lecture 2 (A Simplified Learning Model).
Lecture 3 (PAC Learning).
Lecture 4 (Bias-Complexity Tradeoff).
Lecture 5 (VC Dimension).
Lecture 6 (Fundamental Theorem of Statistical Learning).
Lecture 7 (Linear Predictors).
Lecture 8 (Boosting).
Lecture 9 (Convex Learning Problems).
Lecture 10 (Stochastic Gradient Descent).
Lecture 11 (Regularization and Stability).
Lecture 12 (Support Vector Machine).
Lecture 13 (Kernel Method).