1  Introduction
2 Overview of Supervised Learnina
3 Linear Methods for Regression
4. Linear Methods for Classification
5. Basis Expansions and Regularization

6. Kernel Methods

7. Model Assessment and Selection

8. Model Inference and Averaging

9. Additive Models, Trees, and Related Methods

10. Boosting and Additive Trees

11.Neural Networks

12. Flexible Discriminants

13. Prototypes and Nearest-Neighbors

14. Unsupervised Learning