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Syllabus

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
courses/xep33gmm/materials/lectures.txt · Last modified: 2020/12/15 22:52 by flachbor