Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019 - Bayesian Networks 1 - Inference | Stanford CS221: AI (Autumn 2019)

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-liang Assistant Professor Dorsa Sadigh Assistant Professor in the Computer Science Department & Electrical Engineering Department https://profiles.stanford.edu/dorsa-sadigh To follow along with the course schedule and syllabus, visit: https://stanford-cs221.github.io/autumn2019/#schedule 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