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: Empirical risk minimisation for HMMs; Unsupervised learning: EM algorithm for HMMs | | |
06 | Extensions of Markov models and HMMs: acyclic graphs, uncountable feature and state spaces | | |
07 | Markov Random Fields - Markov models on general graphs. Equivalence to Gibbs models | | |
08 | Searching the most probable state configuration: transforming the task into a MinCut-problem for the submodular case. | | |
09 | Searching the most probable state configuration: approximation algorithms for the general case. | | |
10 | The partition function and marginal probabilities: approximation algorithms for their estimation. | | |
11 | Parameter learning for Gibbs random fields | |
12 | Unsupervised learning for GRFs | | |