Lecture Notes - Statistical Learning Theory

• Syllabus
• lecture 1 PAC Learnability
• lecture 2 Learnability of Infinite Hypothesis Classes
• lecture 3 Bias-Variance Tradeoff and the No-Free-Lunch Theorem
• lecture 4 - Learnability of Infinite Hypothesis Classes
• lecture 5 Non-Uniform Learnability
• lecture 6 Advanced Theory
• lecture 7 Boosting
• lecture 8 Universal Approximation of Neural Networks
• lecture 9 Generalized Linear Models
• lecture 10 Stability, Regularization, and Online Classification
• lecture 11 Modern Theory - Generalization in the Overparameterized Regime