Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
Автор: Stanford Online
Загружено: 2022-05-31
Просмотров: 3711
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai
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
0:06 Bayesian networks: supervised learning
0:15 Review: Bayesian network
1:22 Review: probabilistic inference
2:15 Where do parameters come from?
2:37 Learning task
3:42 Example: one variable
5:41 Example: two variables
8:13 Example: v-structure
11:33 Example: inverted-v structure
15:17 Parameter sharing
18:10 Example: Naive Bayes
19:51 Example: HMMS
22:57 General case: learning algorithm
24:15 Maximum likelihood
30:15 Summary
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