This course teaches students how to apply Hidden Markov Models for probabilistic inference, object tracking, and particle filtering. The course covers topics such as lattice representation, beam search, Gibbs sampling, and resampling techniques. The teaching method includes theoretical explanations, demonstrations, and practical applications. This course is intended for individuals interested in advancing their knowledge of Bayesian Networks and AI.
Overview
Syllabus
Introduction.
Review: Bayesian network.
Review: probabilistic inference.
Hidden Markov model inference.
Lattice representation.
Summary.
Hidden Markov models.
Review: beam search.
Step 1: propose.
weight.
Step 3: resample.
Application: object tracking.
Particle filtering demo.
Roadmap.
Gibbs sampling.
Taught by
Stanford Online
