| Date | Topic | Subtopic | Section | Readings | HW Release | HW Deadline |
|---|---|---|---|---|---|---|
| Week 0 | 0. Math and Code Review | |||||
| Jan. 25 (Tu) | Welcome to CS181 | Ch 1 | HW 1 released (regression) | |||
| Jan. 27 (Th) | Regression | Linear Regression | 2.3-2.5, 2.6.1, 2.7.1 | |||
| Jan. 28 (Fr) | ||||||
| Week 1 | 1. Linear regression | |||||
| Feb. 1 (Tu) | Regression | Probabilistic Linear Regression | 2.6.2, 2.6.3 | |||
| Feb. 3 (Th) | Classification | Linear Classification | 3.1-3.5 | |||
| Feb. 4 (Fr) | ||||||
| Week 2 | 2. Linear and probabilistic classification | |||||
| Feb. 8 (Tu) | Classification | Probabilistic Linear Classification | 3.6 | |||
| Feb. 10 (Th) | Model selection | Model Selection, Frequentist | 2.7, 2.8 | |||
| Feb. 11 (Fr) | HW 2 released (classification and model selection) | HW 1 due | ||||
| Week 3 | 3. Model selection | |||||
| Feb. 15 (Tu) | Model selection | Model Selection, Bayesian | 2.8, 2.9 | |||
| Feb. 17 (Th) | Neural networks | Neural Networks 1 | 4.1-4.4, 4.6 | |||
| Feb. 18 (Fr) | ||||||
| Week 3 | 4. Neural networks | |||||
| Feb. 22 (Tu) | Neural networks | Neural Networks 2 | 4.4 | |||
| Feb. 24 (Th) | SVMs | Support Vector Machines 1 | 5.1-5.3 | |||
| Feb. 25 (Fr) | HW 3 released (neural networks, model selection) | HW 2 due | ||||
| Week 4 | Midterm 1 review | |||||
| Mar. 1 (Tu) | Midterm 1 | |||||
| Mar. 3 (Th) | Ethics | |||||
| Mar. 4 (Fr) | ||||||
| Week 5 | 5. SVMs | |||||
| Mar. 8 (Tu) | SVMs 2 | Support Vector Machines 2 | 5.4 | |||
| Mar. 10 (Th) | Unsupervised learning | Clustering | 6 | |||
| Mar. 11 (Fr) | HW 4 released (ethics, clustering, SVM) | HW 3 due | ||||
| Week 6 | 7. K-means, HAC | |||||
| Mar. 15 (Tu) | Spring break | |||||
| Mar. 17 (Th) | Spring break | 9.1-9.5 | ||||
| Mar. 18 (Fr) | ||||||
| Week 7 | 8. Mixture, EM | |||||
| Mar. 22 (Tu) | Mixture Models | |||||
| Mar. 24 (Th) | PCA | |||||
| Mar. 25 (Fr) | HW 5 released (Mixtures, EM, graphical models) | HW 4 due | ||||
| Week 8 | 9. PCA, topic models | |||||
| Mar. 29 (Tu) | GMs and BNs | Topic Models | 8 | |||
| Mar. 31 (Th) | Graphical Models | |||||
| Apr. 1 (Fr) | Practical released | |||||
| Week 9 | 10. Bayes nets, inference | |||||
| Apr. 5 (Tu) | Inference for Bayes Nets | |||||
| Apr. 7 (Th) | Hidden Markov Models | |||||
| Apr. 8 (Fr) | HW 5 due | |||||
| Week 10 | 11. HMMs, Kalman filters | |||||
| Apr. 12 (Tu) | RL | Markov Decision Processes | SB 3.1-3.6, SB 4.1-4.4 * | |||
| Apr. 14 (Th) | ||||||
| Apr. 15 (Fr) | HW6 released (inference in graphical models, MDPs) | Practical due | ||||
| Week 11 | 12. Reinforced learning, MDPs | |||||
| Apr. 19 (Tu) | Reinforcement Learning 2 | SB 1.1-1.4, SB 6.1-6.5 * | ||||
| Apr. 21 (Th) | Interpretability | |||||
| Apr. 22 (Fr) | ||||||
| Week 12 | Midterm 2 review | |||||
| Apr. 26 (Tu) | Midterm 2 | |||||
| Apr. 28 (Th) | ||||||
| Apr. 29 (Fr) | HW 6 due |