Stanford CS221 | Autumn 2025 | Lecture 14: Bayesian Networks and Learning
Summary
This video is a university-level lecture from Stanford's CS221 'Artificial Intelligence: Principles and Techniques' course, specifically covering Bayesian Networks and Learning. It is highly relevant as educational content designed for students learning core AI/ML concepts and can serve as a valuable resource for both students and educators in the field of 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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