Tuesday & Thursday, 2:30pm to 3:50pm ET, with class Zoom links on the Canvas page.
Lecture recordings are available through Canvas Media Library.
| Date | Topics | Book Chapters | Slides | Notes |
|---|---|---|---|---|
| Thursday, Jan 21 | Intro, ERM framework | 1, 2.0, 2.1, 2.2 | ||
| Tuesday, Jan 26 | Halfspaces and Perceptron |
9.0, 9.1.0, 9.1.2 |
||
| Thursday, Jan 28 | Linear and Polynomial Regression |
9.2 |
||
| Tuesday, Feb 2 | Logistic Regression |
9.3, 12.1.1, 14.0, 14.1.0 |
||
| Thursday, Feb 4 | SGD, Data Prep, and other Practicalities |
14.3.0, 14.5.1 |
||
| Tuesday, Feb 9 | PAC Learning |
2.3, 3 |
||
| Thursday, Feb 11 | The Bias-Complexity Tradeoff |
5 |
||
| Tuesday, Feb 16 | LONG WEEKEND, NO CLASS | |||
| Thursday, Feb 18 | Model Selection, Validation, and Regularization |
11.0, 11.2, 11.3, 13.1, 13.4 |
||
| Tuesday, Feb 23 | Boosting |
10 |
||
| Thursday, Feb 25 | Decision Trees |
18 |
||
| Tuesday, Mar 2 | Learning via Uniform Convergence | 4 | ||
| Thursday, Mar 4 | VC Dimension | 6, 9.1.3 | ||
| Tuesday, Mar 9 | Naive Bayes | 24.0, 24.1, 24.2 | ||
| Thursday, Mar 11 | K-Nearest Neighbors / Fairness in Machine Learning | 19 | ||
| Tuesday, Mar 16 | Support Vector Machines | 15 | ||
| Thursday, Mar 18 | Kernel Methods | 16 | ||
| Tuesday, Mar 23 | Neural Networks | 20.0, 20.1, 20.2, 20.3 | ||
| Thursday, Mar 25 | Backpropagation | 20.6 | ||
| Tuesday, Mar 30 | K-Means | 22.0, 22.2, 22.5 | ||
| Thursday, Apr 1 | Expectation Maximization | 24.4 | ||
| Tuesday, Apr 6 | Principal Component Analysis | 23.0, 23.1 | ||
| Thursday, Apr 8 | Ethics in Machine Learning | |||
| Tuesday, Apr 13 | NO CLASS (READING PERIOD) | |||
| Thursday, Apr 15 | NO CLASS (READING PERIOD) |