Bayesian Networks 2 - Forward-Backward - Stanford CS221: AI
Offered By: Stanford University via YouTube
Course Description
Overview
Learn about advanced concepts in Bayesian networks and probabilistic inference in this Stanford University lecture from the CS221: AI course. Explore hidden Markov models, lattice representations, and particle filtering techniques. Dive into topics such as beam search, object tracking, and Gibbs sampling. Gain a deeper understanding of forward-backward algorithms and their applications in artificial intelligence through comprehensive explanations and demonstrations.
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
Tags
Related Courses
Bioinformatics Algorithms (Part 2)University of California, San Diego via Coursera Elaborazione del linguaggio naturale
University of Naples Federico II via Federica Fundamentals of Dynamic Programming
LinkedIn Learning Dynamic Programming: Applications In Machine Learning and Genomics
University of California, San Diego via edX Finding Mutations in DNA and Proteins (Bioinformatics VI)
University of California, San Diego via Coursera