| Week | Date | Content | Exercise |
|---|---|---|---|
| 1 | 9/12 | Overview of the course | |
| 2 | 9/19 | 1. Statistical learning 2. Important probability and statistics concepts |
|
| 3 | 9/26 | Bayesian network and d-separation | Exercise 1 (due: 10/16 23:59:59) |
| 4 | 10/3 | 1. Naive Bayesian 2. Bayesian network modeling examples |
|
| 5 | 10/10 | Holiday | |
| 6 | 10/17 |
1. Markov Random Fields 2. Point estimation: MLE and MAP (for famous probability distributions) |
Exercise 2 (due: 10/30 23:59:59) |
| 7 | 10/24 | Exponential family and conjugate prior | |
| 8 | 10/31 | 1. Hypothesis testing 2. Midterm review |
|
| 9 | 11/7 | Midterm exam | |
| 10 | 11/14 | EM algorithm | |
| 11 | 11/21 | 1. Monte Carlo methods 2. Inverse transform sampling, reject sampling, and importance sampling 3. Markov chain |
Exercise 3 (due: 12/4 23:59:59) |
| 12 | 11/28 | 1. Markov Chain Monte Carlo: Metropolis-Hastings and Gibbs sampling 2. Latent Dirichlet Allocation with MCMC |
|
| 13 | 12/5 | Variational inference | Exercise 4 (due: 12/18 23:59:59) |
| 14 | 12/12 | Bayesian linear regression: closed-form vs MCMC vs VI | |
| 15 | 12/19 | 1. Gaussian process 2. Summary of the course |
|
| 16 | 12/26 | Final exam | |
| 17 | 1/2 | Holiday | |
| 18 | 1/9 | Flexible learning week: Bayesian neural network |