Chapter 1: Overview of Supervised Learning

Chapter 2: Linear Methods for Regression

Chapter 3: Linear Method for Classification

Chapter 4: Basis Expansions and Regularization

Chapter 5: Kernel Methods

Chapter 6: Model Assessment and Selection

Chapter 7: Model Inference and Averaging

Chapter 8: Additive Models, Trees, and Related Methods

Chapter 9: Boosting and Additive Trees

Chapter 10: Neural Networks

Chapter 11: Support Vector Machines and Flexible Discriminants

Chapter 12: Prototype Methods and Nearest-Neighbors

Chapter 13: Unsupervised Learning