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	1.1 1.2, 1.4, 1.5, 1.10	1.3, 1.11
E2	Bayesian linear regression (pp)	Session2.pdf	2.7, 2.8, 2.9, 2.10, 2.1, 2.3	2.12, 2.13, 2.11, 2.4
E3	Bayesian linear regression (c)	Session 3.ipynb Session 3 (Google Colab) Session 3 (Binder) Session 3 (Solutions)	3.1, 3.2	3.3
E4	Bayesian networks (pp)	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	5.1, 5.2, 5.4, 5.5	5.3, 5.6
E6	Deterministic approximate inference (pp)	TBD
E7	Deterministic approximate inference (c)	TBD
E8	Unsupervised learning (c)	Session11.ipynb Session11 (Google Colab) Session 11 (Binder) Session11 (Solutions)	11.1, 11.3	11.2
E9	Semi-supervised and generative models (c)	TBD

pp = pen and paper, c = computer