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 Download Session1.pdf | 1.1, 1.3, 1.4, 1.9 | 1.2, 1.10 |
| 2 | Bayesian linear regression (pp) | Session2.pdf Download Session2.pdf | 2.7, 2.8, 2.9, 2.1, 2.3 | 2.11, 2.12, 2.10, 2.4 |
| 3 | Bayesian linear regression (c) | 3.1, 3.2 | 3.3 | |
| 4 | Gaussian processes (c) (only 1RT003) |
Session4.pdf Download Session4.pdf |
4.1, 4.2 | 4.3 |
| 5 | Gaussian processes (c) (only 1RT003) |
Session5.pdf Download Session5.pdf |
5.1, 5.2 | 5.3 |
| 6 | Bayesian networks (pp) | Session6.pdf Download Session6.pdf | 6.1, 6.4, 6.5, 6.7, 6.8 | 6.2, 6.3, 6.6, 6.9 |
| 7 | Monte Carlo methods (pp/c) | Session7.pdf Download Session7.pdf | 7.1, 7.2, 7.4, 7.5 | 7.3, 7.6 |
| 8 | Message passing (pp) | Session8.pdf Download Session8.pdf | 8.1, 8.4 | 8.2, 8.3 |
| 9 | Message passing (pp/c) | Session9.pdf Download Session9.pdf | 9.1, 9.2 | 9.3 |
| 10 | Variational inference (pp/c) (only 1RT003) | Session10.pdf Download Session10.pdf | 10.1a, 10.2, 10.3a | 10.1b, 10.3b, 10.4 |
| 11 | Unsupervised learning (c) |
Session11.ipynb Links to an external site. Session11 (Google Colab) Links to an external site. |
11.1, 11.2 | 11.3 |
Exercise session 4, 5 and 10 only covers material related to the course content 1RT003. Note that in the schedule the lectures are numbered according to the table above, also for 1RT705.
pp = pen and paper, c = computer