Example: HMMS(00:19:51 - 00:22:57) - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: HMMS(00:19:51 - 00:22:57)
Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

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-liang

Assistant Professor Dorsa S...
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-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/autumn2021/#schedule

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

#Stanford #AI
Introduction - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Introduction

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:00:00 - 00:00:06
Bayesian networks: supervised learning - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Bayesian networks: supervised learning

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:00:06 - 00:00:15
Review: Bayesian network - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Review: Bayesian network

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:00:15 - 00:01:22
Review: probabilistic inference - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Review: probabilistic inference

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:01:22 - 00:02:15
Where do parameters come from? - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Where do parameters come from?

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:02:15 - 00:02:37
Learning task - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Learning task

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:02:37 - 00:03:42
Example: one variable - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: one variable

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:03:42 - 00:05:41
Example: two variables - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: two variables

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:05:41 - 00:08:13
Example: v-structure - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: v-structure

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:08:13 - 00:11:33
Example: inverted-v structure - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: inverted-v structure

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:11:33 - 00:15:17
Parameter sharing - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Parameter sharing

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:15:17 - 00:18:10
Example: Naive Bayes - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: Naive Bayes

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:18:10 - 00:19:51
Example: HMMS - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: HMMS

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:19:51 - 00:22:57
General case: learning algorithm - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

General case: learning algorithm

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:22:57 - 00:24:15
Maximum likelihood - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Maximum likelihood

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:24:15 - 00:30:15
Summary - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Summary

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日 
00:30:15 - 00:31:44

Stanford Online

※本サイトに掲載されているチャンネル情報や動画情報はYouTube公式のAPIを使って取得・表示しています。

Timetable

動画タイムテーブル

動画数:2418件