The course teaches students how to specify joint distributions compactly using Bayesian networks and factor graphs. It covers probabilistic inference, explaining away, consistency of sub-Bayesian networks, and applications such as medical diagnosis, language modeling, object tracking, multiple object tracking, and document classification. The course aims to equip learners with the skills to model complex systems and make informed decisions using probabilistic programming. The intended audience for this course is individuals interested in artificial intelligence, machine learning, and probabilistic graphical models.
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
