| 1 | 1/21 | Introduction (Ch1) | 1/23 | Supervised Learning (Ch2) |
| 2 | 1/28 | Review (Linear Algebra, Probability) HW0 due (1/28) | 1/30 | ML Programming |
| 3 | 2/4 | Bayesian Decision Theory and Parametric Methods (Ch3,4) | 2/6 | Multivariate Methods (Ch5) (HW1 posted) |
| 4 | 2/11 | Dimensionality Reduction (Ch6) | 2/13 | (Quiz 1) |
| 5 | 2/18 | Clustering (Ch7, (B*)Ch9) | 2/20 | HW1 due (2/21), (HW2 posted) |
| 6 | 2/25 | Non-parametric Methods (Ch8) | 2/27 | (Quiz 2) |
| 7 | 3/4 | Linear Discrimination (Ch10); Midterm Review | 3/6 | HW2 due (3/9) |
| 8 | 3/11 | Spring Break | 3/13 | Enjoy your break! |
| 9 | 3/18 | Neural Networks (Ch11) | 3/20 | Neural Networks |
| 10 | 3/25 | Midterm (3/25) | 3/27 | Modern Deep Learning, (HW3 posted) |
| 11 | 4/1 | Backprop Recap & Midterm Discussion | 4/3 | (Quiz 3) |
| 12 | 4/8 | Kernel Machines (Ch14) | 4/10 | HW3 due (4/11) |
| 13 | 4/15 | Decision Trees (Ch9) | 4/17 | (HW4 posted) |
| 14 | 4/22 | Random Forests (Ch9); Model Comparison and Case Studies | 4/24 | (Quiz 4) |
| 15 | 4/29 | Graphical Models (Ch15); Final Review | 5/1 | HW4 due (5/2) |
| 16 | 5/6 | TBD | 5/8 | - (Last day of instruction 5/6) |
| Final | 5/12 | 8am-10am | — | — |