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.
All references to supplementary material is (if nothing else noted) to James et al 2013: An Introduction to Statistical Learning. These are not part of the curriculum but can be useful supporting material.
- Chapter 1 (introduction)
- All
- Supplementary: Chapter 1
- Chapter 2 (Overview of supervised learning)
- 2.1-2-7, 2.9
- Supplementary: Chapter 2
- 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
- Supplementary: Chapter 3, Chapter 6 (not 6.3.2)
- Chapter 4: Linear methods for classification
- 4.1, 4.2, 4.3 (not 4.3.3), 4.4 (not 4.4.3)
- Supplementary: Chapter 4
- Chapter 5: Basis expansions and regularization
- Sections 5.1-5.6 (in sec 5.4, only the first two paragraphs and eq (5.16)
- Supplementary: Chapter 7
- Chapter 6: Kernel smoothing methods
- Sections 6.1-6.3
- Supplementary: Sections 3.5, 4.6.5
- Chapter 7: Model assessment and selection
- Sections 7.1-7.3 and 7.6 and 7.10 (perhaps 7.4, 7.5)
- Supplementary: Sec 2.2
- Chapter 9: Additive models, trees and related methods
- Supplementary: Chapter 8
- Chapter 11: Neural networks
- All, except sec 11.2 and 11.9
- Supplementary: The note on neural networks
- Chapter 14: Sec 14.1, 14.3 (not 14.3.9)
- All given exercises