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 |
| E1 | Probabilistic modelling (pp) | Session1.pdf Download Session1.pdf | 1.1, 1.3, 1.4, 1.9 | 1.2, 1.10 |
| E2 | 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 |
| E3 | Bayesian linear regression (c) |
Session 3.ipynb Links to an external site. Session 3 (Google Colab) Links to an external site. |
3.1, 3.2 | 3.3 |
| E4 | Bayesian networks (pp) | Session4.pdf Download Session4.pdf | 4.1, 4.4, 4.5, 4.7, 4.8 | 4.2, 4.3, 4.6, 4.9 |
| E5 | Monte Carlo methods (pp/c) | Session5.pdf Download Session5.pdf | 5.1, 5.2, 5.4, 5.5 | 5.3, 5.6 |
| E6 | Message passing (pp) | Session6.pdf Download Session6.pdf | 6.1, 6.4 | 6.2, 6.3 |
| E7 | Message passing (pp/c) | 7.1., 7.2 | 7.3 | |
| E8 | Gaussian processes (c) (only 1RT003) |
Session8.ipynb Links to an external site. Session 8 (Colab) Links to an external site. |
8.1, 8.2, 8.3 | 8.4 |
| E9 | Gaussian processes (c) (only 1RT003) |
Session9.ipynb Links to an external site. Session 9 (Colab) Links to an external site. |
9.1, 9.2 | 9.3, 9.4 |
| E10 | Variational inference (pp/c) (only 1RT003) | Session10.pdf Download Session10.pdf | 10.1, 10.2 | 10.3, 10.4 |
| E11 | Unsupervised learning (c) |
Session11.ipynb Links to an external site. Session11 (Google Colab) Links to an external site. |
11.1, 11.3 | 11.2 |
Exercise session 8, 9, 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