| Lect. | Lecturer | Topic | Pdf |
| 01 | BF | Markov Random Fields & Gibbs Random Fields | |
| 02 | BF | The most probable realisation of a GRF | |
| 03 | BF | Belief Networks & Stochastic Neural Networks | |
| 04 | VF | Discriminative structured output learning, Perceptron algorithm I | |
| 05 | VF | Discriminative structured output learning, Perceptron algorithm II | |
| 06 | VF | Learning max-sum classifier by Perceptron | |
| 07 | VF | Structured Output Support Vector Machines I | |
| 08 | VF | Structured Output Support Vector Machines II | |
| 09 | VF | Learning Max-Sum classifier by SO-SVM | +Joachims05 |
| 10 | BF | Maximum Likelihood learning for MRFs I | |
| 11 | BF | Maximum Likelihood learning for MRFs II | |
| 12 | BF | Variational Bayesian inference for DNNs | |
| 13 | BF | Variational Autoencoders | |
| 14 | BF | Generative adversarial networks | |