Advanced Probabilistic Machine Learning - Exercise sessions
The material include a relatively rich set of problems. We list a few of them as recommended, but we encourage you to read and reflect upon all problems (even if you do not solve them).
| # | Topic | Material | Recommended problems | Additional problems |
|---|---|---|---|---|
| 1 | Probabilistic modelling (pp) | Session1.pdf |
1.1, 1.3, 1.4, 1.9 | 1.2, 1.10 |
| 2 | Bayesian linear regression (pp) | Session2.pdf |
2.7, 2.8, 2.9, 2.1, 2.3 | 2.11, 2.2, 2.4, 2.10 |
| 3 | Bayesian networks (pp) | Session3.pdf |
3.1, 3.4, 3.5, 3.7, 3.8 | 3.2, 3.3, 3.6, 3.9 |
| 4 | Monte Carlo methods (pp/c) | Session4.pdf |
4.1, 4.2, 4.4, 4.5 | 4.3, 4.6 |
| 5 | Factor graphs (pp) | Session5.pdf |
5.1, 5.4 | 5.2, 5.3 |
| 6 | Variational inference & Expectation propagation (pp) | Session6.pdf |
6.3ab, 6.2, 6.4a | 6.1a |
| 7 | Message passing (c) | Session7.pdf |
7.1 | 7.2 |
| 8 | Gaussian processes (c) |
Session8.pdf |
8.1, 8.2 | 8.3 |
| 9 | Gaussian processes (c) | Session9.pdf |
9.1, 9.2 | 9.3 |
pp = pen and paper, c = computer
(Note! In a previous version of the schedule, there were 10 sessions scheduled. Session 10 is cancelled and replaced with an helpdesk session for the lab.)