Textbook: Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome: The Elements of Statistical Learning, 2009. 2nd edition, Springer.
Curriculum:
- Chapter 1: Introduction
- Chapter 2: Overview of Supervised Learning
- Chapter 3: Linear Methods for Regression
- Chapter 5: Basis Expansions and Regularization
- Chapter 6: Kernel Smoothing Methods
- Chapter 7: Model Assessment and Selection
- Chapter 8: Model Inference and Averaging
- Chapter 9: Additive Models, Trees, and Related Methods
- Chapter 10: Boosting and Additive Trees
- Chapter 11: Neural Networks
- Chapter 13: Prototype Methods and Nearest-Neighbors
- Chapter 16: Ensemble Learning
- Chapter 18: High-Dimensional Problems: p>>n