| Week | Date | Content | Exercise |
|---|---|---|---|
| 1 | 9/13 | Overview of the course | |
| 2 | 9/20 | Holiday | |
| 3 | 9/27 | 1. Statistical learning 2. Important probability and statistics concepts |
|
| 4 | 10/4 | 1. Bayesian network and d-separation 2. Modeling examples and Naive Bayesian |
|
| 5 | 10/11 | Holiday | |
| 6 | 10/18 | 1. Naive Bayesian 2. Markov Random Fields |
Exercise 1 (due: 10/31) |
| 7 | 10/25 | 1. Markov Random Fields 2. Inference by variable elimination and message passing |
|
| 8 | 11/1 | 1. Inference by message passing and junction tree 2. Point estimation: MLE and MAP |
Exercise 2 (due: 11/14) |
| 9 | 11/8 | 1. MAP 2. Hypothesis testing 3. Midterm review |
|
| 10 | 11/15 | Midterm exam | |
| 11 | 11/22 | 1. Exponential family and conjugate prior | |
| 12 | 11/29 | 1. K-means and GMM 2. EM algorithm |
Exercise 3 (due: 12/12) |
| 13 | 12/6 | 1. EM algorithm 2. Monte Carlo sampling |
|
| 14 | 12/13 | 1. Gibbs sampling 2. Markov chain 3. Metropolis-Hastings and Markov Chain Monte Carlo |
|
| 15 | 12/20 | 1. MCMC for LDA 2. Variational inference 3. Ising model |
Exercise 4(due: 1/2) |
| 16 | 12/27 | 1. Bayesian linear regression 2. Gaussian process |
|
| 17 | 1/3 | 1. Multi-Armed Bandit and Bayesian optimization 2. Final exam review |
|
| 18 | 1/10 | Final exam |