| Chapter 1.1 | Concepts of Learning Notes, Ockham's Razor Basics Slides |
| Chapter 1.2 | ROC Curves Slides, Part I, ROC Curves Slides, Part II, ROC Curves Notes, ROC Curves Exploratory Data Analysis |
| Chapter 1.3 | Cross Validation Slides, Cross Validation Notes |
| Chapter 2.1 | Decision Trees Notes, Decision Trees Slides |
| Chapter 2.2 | Modern Decision Trees Notes, GOSDT Computation Example Walkthrough by Chudi Zhong |
| Chapter 3.1 | Random Forest Slides, Random Forest Notes |
| Chapter 3.2 | Variable Importance Notes |
| Chapter 3.3 | Boosting Notes, Boosting Slides |
| Chapter 3.4 | Interpretable Generalized Additive Models via Boosting |
| Chapter 4 | Logistic Regression |
| Chapter 5.1 | Convex Optimization |
| Chapter 5.2 | Support Vector Machines |
| Chapter 5.3 | Kernels Notes, Kernels Slides |
| Chapter 6 | Statistical Learning Theory Notes, Statistical Learning Theory Slides |
| Chapter 7 | Least Squares and Friends Notes |
| Chapter 8 | Dimension Reduction for Data Visualization |
| Chapter 9 | Perceptron and Winnow Notes |
| Chapter 10 | Clustering Notes, Clustering Slides |
| Chapter 11 | Gaussian Mixture Models and Expectation Maximization |
| Chapter 12.1 | Neural Networks Slides |
| Chapter 12.2 | Cross Entropy is Logistic Loss |
| Chapter 13 | Multi-armed Bandit Notes, Multi-armed Bandit Slides
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