Textbook: Hastie et al (2009)
Book-order:
Chapter 1: Introduction
Chapter 2: Overview of Supervised Learning (§2.1 -- §2.3, §2.5, §2.9)
Chapter 3: Linear Models for Regression (§3.1 -- §3.6, except §3.2.1 -- §3.2.4, §3.4.4)
Chapter 4: Linear Methods for Classification (§4.1 -- §4.4, except §4.3.3, §4.4.3)
Chapter 5: Basis Expansions and Regularization (§5.1, §5.2, §5.4, §5.5)
Chapter 6: Kernel Smoothing Methods (§6.1 -- §6.3)
Chapter 7: Model Assessment and Selection (§7.1 -- §7.7, §7.10, §7.11)
Chapter 8: Model Inference and Averaging (§§8.7)
Chapter 9: Additive Models, Trees, and Related Methods (§9.1, §9.2)
Chapter 10: Boosting and Additive Trees (§10.1, $10.3, §10.9)
Chapter 11: Neural Networks (§11.1 -- §11.3)
Chapter 12: Support Vector Machines and Flexible Discriminants (§12.1 -- §12.3)
Chapter 13: Prototype Methods and Nearest-Neighbors (§13.3)
Chapter 14: Unsupervised Learning (§14.1, §14.3)
Chapter 15: Random Forests (§15.1 -- §15.3)
Chapter 18: High-Dimensional Problems: p >> N (§18.4)
Lectures-order:
Ch 1, §2.1 -- §2.3.1, §3.1 -- §3.3 (except §3.2.1 -- §3.2.4), §2.9, §7.1 -- §7.3, §7.10, §7.4, §7.5, §7.7 (only up to page 233), §7.11, §3.5, §3.4 (§3.4.4), §18.4, §3.6, §2.3.2, §2.3.3, §2.5, §6.1 -- §6.3, §5.1, §5.2, §5.4, §5.5, §9.1, §9.2.1, §9.2.2, §8.7, §15.1 -- §15.3, §10.1, $10.3, §11.1 -- §11.3, §4.1 -- §4.4 (except §4.3.3, §4.4.3), §13.3, §9.2.3, §9.2.4, §10.9, §12.1 -- §12.3, §14.1, §14.3.