Bayesian Networks 1 – Inference – Stanford CS221: AI

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Overview

Bayesian Networks 1 - Inference is a specialized module from Stanford’s CS221 Artificial Intelligence course that focuses on understanding and performing inference in Bayesian networks. These networks are powerful probabilistic graphical models used to represent and reason about uncertainty in complex systems. The course begins by introducing the structure of Bayesian networks, explaining how nodes represent random variables and edges encode conditional dependencies. You will learn fundamental inference techniques such as exact inference methods including variable elimination and belief propagation, which...

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.