YoVDO

Bayesian Networks 2 - Forward-Backward - Stanford CS221: AI

Offered By: Stanford University via YouTube

Tags

Statistics & Probability Courses Artificial Intelligence Courses Hidden Markov Models Courses Bayesian Networks Courses Object Tracking Courses Gibbs Sampling Courses Probabilistic Inference Courses

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

Introduction to Artificial Intelligence
Stanford University via Udacity Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Artificial Intelligence for Robotics
Stanford University via Udacity Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent