Learn about Hidden Markov Models (HMM) in this 34-minute lecture focusing on parameter estimation, inference, and the Viterbi algorithm. Explore the mathematical foundations and practical applications of HMMs, understanding how to estimate model parameters and perform efficient sequence analysis using dynamic programming techniques. Dive into the Viterbi algorithm's mechanics for finding the most likely sequence of hidden states in an HMM, with detailed explanations supported by comprehensive slides and examples.
Hidden Markov Models: Parameter Estimation, Inference, and Viterbi Algorithm
Overview
Syllabus
HMM: Parameter estimation & inference; Viterbi
Taught by
UofU Data Science
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