| Lect. | Topic | Pdf |
| 01 | Markov chains, equivalent representations, ergodicity, convergence theorem for homogeneous Markov chains. | |
| 02 | Hidden Markov Models on chains for speech recognition: pre-processing, dynamic time warping, HMM-s. | |
| 03 | Inference tasks for Hidden Markov Models | |
| 04 | HMMs as exponential families, supervised learning: maximum likelihood estimator | |
| 05 | Supervised learning: Emprirical risk minimisation for HMMs; Unsupervised learning: EM algorithm for HMMs | |
| 06 | Supervised learning: Emprirical risk minimisation for HMMs; Unsupervised learning: EM algorithm for HMMs(cont'd) | |
| 07 | Extensions of Markov models and HMMs: acyclic graphs, uncountable feature and state spaces | |
| 08 | Markov Random Fields - Markov models on general graphs. Equivalence to Gibbs models | |
| 09 | Searching the most probable state configuration: transforming the task into a MinCut-problem for the submodular case. | |
| 10 | Searching the most probable state configuration: approximation algorithms for the general case. | |
| 11 | Searching the most probable state configuration: approximation algorithms for the general case. (cont'd) | |
| 12 | The partition function and marginal probabilities: approximation algorithms for their estimation. | |
| 13 | Parameter learning for Gibbs random fields | |
| 14 | Reserve | |