Lecture

Hidden Markov Models: Primer

Description

This lecture provides a primer on Hidden Markov Models (HMMs), covering the definition of Markov Models, the three basic problems for HMMs, and the algorithms to solve them: Forward-Backward, Viterbi, and Baum-Welch. It explains the concepts with examples and details the Expectation-Maximization algorithm for unsupervised learning.

Instructors (3)

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Official source

https://mediaspace.epfl.ch/media/0_2dshkff9

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Ontological neighbourhood

Information engineering

Natural language processing: Topics in natural language processing

Statistics

Statistical inference: Bayesian inference

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