| Week | Date | Content | Exercise |
|---|---|---|---|
| 1 | 9/9 | Overview of the course | |
| 2 | 9/16 | 1. Statistical learning 2. Important probability and statistics concepts |
|
| 3 | 9/23 | Bayesian network and d-separation | Exercise 1 (due: 10/6 23:59:59) |
| 4 | 9/30 | 1. Naive Bayesian 2. Bayesian network modeling examples |
|
| 5 | 10/7 |
1. Markov Random Fields 2. Point estimation: MLE and MAP (for famous probability distributions) |
Exercise 2 (due: 10/20 23:59:59) |
| 6 | 10/14 | Exponential family and conjugate prior | |
| 7 | 10/21 | 1. Hypothesis testing 2. Midterm review |
|
| 8 | 10/28 | Midterm exam | |
| 9 | 11/4 | EM algorithm | |
| 10 | 11/11 | Monte Carlo methods | Exercise 3 (due: 11/24 23:59:59) |
| 11 | 11/18 | Variational inference | |
| 12 | 11/25 | 1. Monte Carlo methods 2. Inverse transform sampling, reject sampling, and importance sampling 3. Markov chain |
|
| 13 | 12/2 | 1. Markov Chain Monte Carlo: Metropolis-Hastings and Gibbs sampling 2. Latent Dirichlet Allocation with MCMC 3. Bayesian linear regression: closed-form vs MCMC vs VI |
Exercise 4 (due: 12/15 23:59:59) |
| 14 | 12/9 | 1. Gaussian process 2. Summary of the course |
|
| 15 | 12/16 | Invited Talk | |
| 16 | 12/23 | Final exam | |
| 17 | 12/30 | Flexible learning week: Bayesian neural network | |
| 18 | 1/6 | Flexible learning week: Interpretable machine learning |