All main references correspond to Hastie et al. (2009): Elements of Statistical learning). So far whole chapters are included, but some parts will not be included in the curriculum.
- Chapter 1 (introduction): All
- Chapter 2 (Overview of supervised learning): 2.1-2.6, 2.9
- Chapter 3 Linear methods for regression: 3.1-3.4 (not 3.2.3, 3.2.4 and 3.4.4), 3.5.1, 3.6
- Chapter 4 Linear methods for classification: 4.1, 4.2, 4.3 (not 4.3.3), 4.4 (not 4.4.3)
- Chapter 5 Basis expansions and regularisation: Sections 5.1-5.6 (in sec 5.4, only the first two paragraphs and eq (5.16)
- Chapter 6 Kernel smoothing methods: Sections 6.1-6.3
- Chapter 7 Model assessment and selection: Sections 7.1-7.7 and 7.10-7.11
- Chapter 9 Additive models, trees and related methods: Sections 9.1, 9.2 and 9.4
- Chapter 10 Boosting and Additive Trees: Sections 10.1, 10.3 and 10.9)
- Chapter 11 Neural networks: All, except sec 11.2 and 11.9, and a supplementary note on neural networks
- Chapter 12 Support Vector Machines and Flexible Discriminants: Sections12.1-12.3
- Chapter 13 Prototype Methods and Nearest-Neighbors: Section 13.3
- Chapter 14: Sec 14.1, 14.3 (not 14.3.9)
- Chapter 15 Random Forests: Sections 15.1-15.3
- All given exercises