| 1 | Introduction | 01/ |
| 2 | Probability | 02/ |
| 3 | Bayesian statistics | 03/ |
| 4 | Probabilistic graphical models | 04/ |
| 5 | Information theory | 05/ |
| 6 | Inference algorithms: an overview | inf/ |
| 7 | Optimization | opt/ |
| 8 | Inference for state-space models | 08/ |
| 9 | Inference for graphical models | 09/ |
| 10 | Variational inference | 10/ |
| 11 | Monte Carlo inference | 11/ |
| 12 | Markov Chain Monte Carlo inference | 12/ |
| 13 | Sequential Monte Carlo inference | 13/ |
| 14 | Predictive models: an overview | 14/ |
| 15 | Generalized linear models | 15/ |
| 16 | Deep neural networks | 16/ |
| 17 | Bayesian neural networks | 17/ |
| 18 | Gaussian processes | 18/ |
| 19 | Beyond the iid assumption | 19/ |
| 20 | Generative models: an overview | 20/ |
| 21 | Variational autoencoders | 21/ |
| 22 | Auto-regressive models | 22/ |
| 23 | Normalizing Flows | 23/ |
| 24 | Energy-based models | 24/ |
| 25 | Diffusion models | 25/ |
| 26 | Generative adversarial networks | 26/ |
| 27 | Discovery methods: an overview | 27/ |
| 28 | Latent factor models | 28/ |
| 29 | State-space models | 29/ |
| 30 | Graph learning | 30/ |
| 31 | Non-parametric Bayesian models | 31/ |
| 32 | Representation learning | 32/ |
| 33 | Interpretability | 33/ |
| 34 | Decision making under uncertainty | 34/ |
| 35 | Reinforcement learning | 35/ |
| 36 | Causality | 36/ |
