| Date | Topic | Subtopic | Section | Readings | HW Release | HW Deadline |
|---|---|---|---|---|---|---|
| 0. Math and Code Review | ||||||
| Jan. 26 (T) | Introduction | 2.1, 2.2.1 | HW 1 (Regression) released | |||
| Jan. 28 (Th) | Regression | Linear Regression | 2.3-2.5, 2.6.1, 2.7.1 | |||
| Jan. 29 (Fr) | ||||||
| 1. Linear Regression, MLE | ||||||
| Feb. 2 (T) | Probabilistic Linear Regression | 2.6.2, 2.6.3 | ||||
| Feb. 4 (Th) | Classification | Linear Classification | 3.1-3.5 | HW 1 due | ||
| Feb. 5 (Fr) | HW 2 (Classification, Bayes) released | |||||
| 2. Probabilistic Classification | ||||||
| Feb. 9 (T) | Probabilistic Linear Classification | 3.6 | ||||
| Feb. 11 (Th) | Model Selection | Model Selection - Frequentist | 2.7, 2.8 | |||
| Feb. 12 (Fr) | ||||||
| 3. | ||||||
| Feb. 16 (T) | Model Selection - Bayesian | 2.8, 2.9 | ||||
| Feb. 18 (Th) | Neural Networks | Neural Networks 1 | 4.1-4.4, 4.6 | |||
| Feb. 19 (Fr) | HW 3 (Bayes, Neural Networks) released | HW 2 due | ||||
| 4. | ||||||
| Feb. 23 (T) | Neural Networks 2 | 4.4 | ||||
| Feb. 25 (Th) | SVMs | Support Vector Machines 1 | 5.1-5.3 | |||
| Feb. 26 (Fr) | ||||||
| 5. | ||||||
| Mar. 2 (T) | Support Vector Machines 2 | 5.4 | ||||
| Mar. 4 (Th) | EthiCS Guest Lecture | |||||
| Mar. 5 (Fr) | HW 4 (SVMs, Ethics, Clustering) released | HW 3 due | ||||
| 6. | ||||||
| Mar. 9 (T) | Unsupervised Learning | Clustering | 6 | |||
| Mar. 11 (Th) | Midterm 1 | |||||
| Mar. 12 (Fr) | ||||||
| 7. | ||||||
| Mar. 16 (T) | Wellness day, no class | |||||
| Mar. 18 (Th) | Mixture Models | 9.1-9.5 | ||||
| Mar. 19 (Fr) | HW 5 (Mixtures, EM, Graphical) released | HW 4 due | ||||
| 8. | ||||||
| Mar. 23 (T) | Principal Component Analysis | 7 | ||||
| Mar. 25 (Th) | Topic Models | 9.6 | ||||
| Mar. 26 (Fr) | Practical released | |||||
| 9. | ||||||
| Mar. 30 (T) | GMs and BNs | Graphical Models | 8 | |||
| Apr. 1 (Th) | Inference for Bayes Nets | |||||
| Apr. 2 (Fr) | HW 5 due | |||||
| 10. | ||||||
| Apr. 6 (T) | Hidden Markov Models | 10 | ||||
| Apr. 8 (Th) | Markov Decision Processes | SB 3.1-3.6, SB 4.1-4.4 * | ||||
| Apr. 9 (Fr) | HW 6 (Inference, MDPs) released | Practical due | ||||
| 11. | ||||||
| Apr. 13 (T) | RL | Reinforcement Learning 1 | ||||
| Apr. 15 (Th) | Wellness day, no class | |||||
| Apr. 16 (Fr) | ||||||
| 12. | ||||||
| Apr. 20 (T) | Reinforcement Learning 2 | SB 1.1-1.4, SB 6.1-6.5 * | ||||
| Apr. 22 (Th) | Interpretability | |||||
| Apr. 23 (Fr) | HW 6 due | |||||
| 13. | ||||||
| Apr. 27 (T) | Midterm 2 |