Bayesian Networks 1 - Inference - Stanford CS221: AI
Offered By: Stanford University via YouTube
Course Description
Overview
Syllabus
Introduction.
Announcements.
Pac-Man competition.
Review: definition.
Review: object tracking.
Course plan.
Review: probability Random variables: sunshine S € (0,1), rain R € {0,1}.
Challenges Modeling: How to specify a joint distribution P(X1,...,x.) compactly? Bayesian networks (factor graphs to specify joint distributions).
Probabilistic inference (alarm).
Explaining away.
Consistency of sub-Bayesian networks.
Medical diagnosis.
Summary so far.
Roadmap.
Probabilistic programs.
Probabilistic program: example.
Probabilistic inference: example Query: what are possible trajectories given evidence.
Application: language modeling.
Application: object tracking.
Application: multiple object tracking.
Application: document classification.
Taught by
Stanford Online
Tags
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