| Lect. | Lecturer | Topic | Pdf | Additional reading |
| 01 | BF | Markov Random Fields & Gibbs Random Fields | | |
| 02 | BF | The most probable realisation of a GRF | | [Savchynskyy19] |
| 03 | BF | Belief Networks & Stochastic Neural Networks | | |
| 04 | VF | Empirical Risk Minimization | | |
| 05 | VF | Structured Output Linear Classifier and Perceptron | | |
| 06 | VF | Learning max-sum classifier by Perceptron | | |
| 07 | VF | Structured Output Support Vector Machines | | |
| 08 | VF | Cutting Plane Algorithm | | |
| 09 | VF | Learning Max-Sum classifier by SO-SVM | | |
| 10 | BF | Maximum Likelihood learning for MRFs I | | updated on 4.5.22 |
| 11 | BF | Maximum Likelihood learning for MRFs II | | |
| 12 | BF | Variational Autoencoders | | |