Stanford CS221 | Autumn 2025 | Lecture 13: Bayesian Networks and Gibbs Sampling
Summary
This video is Lecture 13 from Stanford's CS221 course on Artificial Intelligence, focusing on Bayesian Networks and Gibbs Sampling. It provides an in-depth explanation of these fundamental AI/ML concepts, making it highly valuable for students and educators learning about probabilistic graphical models and inference techniques in AI.
Description
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai To learn more about enrolling in this course, visit: https://online.stanford.edu/courses/cs221-artificial-intelligence-principles-and-techniques Please follow along with the course schedule: https://stanford-cs221.github.io/autumn2025/ Follow the playlist: https://youtube.com/playlist?list=PLoROMvodv4rMeDqwS1yFl3j3sR_-MQNEN&si=bVivXjDfVEQKky1D Teaching Team Percy Liang, Associate Professor of Computer Science (and courtesy in Statistics)
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