For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3bcQMeG

Topics: Bayesian Networks
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University
http://onlinehub.stanford.edu/

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-l...

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department
https://profiles.stanford.edu/dorsa-s...

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autu...

0:00 Introduction
0:22 Announcements
2:11 Pac-Man competition
4:54 Review: definition
6:06 Review: object tracking
7:50 Course plan
9:09 Review: probability Random variables: sunshine S € (0,1), rain R € {0,1}
15:50 Challenges Modeling: How to specify a joint distribution P(X1,...,x.) compactly? Bayesian networks (factor graphs to specify joint distributions)
28:48 Probabilistic inference (alarm)
30:45 Explaining away
37:02 Consistency of sub-Bayesian networks
45:34 Medical diagnosis
46:16 Summary so far
47:27 Roadmap
47:42 Probabilistic programs
50:40 Probabilistic program: example
52:32 Probabilistic inference: example Query: what are possible trajectories given evidence
55:28 Application: language modeling
56:15 Application: object tracking
57:58 Application: multiple object tracking
59:09 Application: document classification