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courses
xep33gmm
materials
lectures
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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: Emprirical risk minimisation for HMMs
06
Unsupervised learning: EM algorithm for HMMs
07
Extensions of Markov models and HMMs: acyclic graphs, uncountable feature and state spaces
08
Extensions of Markov models and HMMs: acyclic graphs, uncountable feature and state spaces (cont'd)
09
Markov Random Fields - Markov models on general graphs. Equivalence to Gibbs models
10
Searching the most probable state configuration: transforming the task into a MinCut-problem for the submodular case.
11
Searching the most probable state configuration: approximation algorithms for the general case.
12
Searching the most probable state configuration: approximation algorithms for the general case. (cont'd)
13
The partition function and marginal probabilities: approximation algorithms for their estimation.
14
Parameter learning for Gibbs random fields
courses/xep33gmm/materials/lectures.txt
· Last modified: 2018/01/03 10:44 by
flachbor