Bayesian Networks 3 - Maximum Likelihood | Stanford CS221: AI (Autumn 2019)

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...

Bayesian Networks 3 - Maximum Likelihood | Stanford CS221: AI (Autumn 2019)

Stanford Online
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2020
Dec 17
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Zlc5Iu

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
0:18 Announcements
2:00 Review: Bayesian network
2:57 Review: probabilistic inference
4:13 Where do parameters come from?
4:37 Roadmap
5:02 Learning task
6:29 Example: one variable
12:06 Example: v-structure
14:48 Example: inverted-v structure
20:28 Parameter sharing
21:35 Example: Naive Bayes
26:05 Example: HMMS
33:40 General case: learning algorithm
36:14 Maximum likelihood
41:05 Scenario 2
42:45 Regularization: Laplace smoothing
44:14 Example: two variables
49:09 Motivation
49:49 Maximum marginal likelihood
52:59 Expectation Maximization (EM)