Bayesian Networks 1 - Inference - Stanford CS221: AI

Learn about Bayesian networks and probabilistic inference in this Stanford CS221 lecture on artificial intelligence. Explore key concepts such as joint distributions, factor graphs, and explaining away. Discover applications in medical diagnosis, language modeling, object tracking, and document classification. Gain insights into probabilistic programming and its practical implementations. Understand how to specify joint distributions compactly and perform inference in various scenarios. Follow along as the lecture covers the Pac-Man competition, reviews probability basics, and delves into the challenges of modeling complex systems. Enhance your understanding of AI techniques for reasoning under uncertainty and making informed decisions based on probabilistic evidence.

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.