courses:xep33gmm:materials:lectures [CourseWare Wiki]

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