投稿

【ML】斯坦福CS229：机器学习中英文字幕 by Andrew Ng

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2020-04-22 20:20:20

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课程说明：本课程对机器学习和统计模式识别进行了广泛的介绍。主题包括：有监督的学习（生成/区分学习，参数/非参数学习，神经网络，支持向量机）；无监督学习（聚类，降维，核方法）；学习理论（偏见/方差折衷，实用建议）；强化学习和自适应控制。本课程还将讨论机器学习的最新应用，例如机器人控制，数据挖掘，自主导航，生物信息学，语音识别以及文本和Web数据处理。

MACHINELEARNING

大佬的迷弟的粉丝发消息

致力于划水摸鱼的佛系研究生

1. Lecture 1 - Welcome

01:15:20

2. Lecture 2 - Linear Regression and Gradient Descent

01:18:17

3. Lecture 3 - Locally Weighted & Logistic Regression

01:19:35

4. Lecture 4 - Perceptron & Generalized Linear Model

01:22:02

5. Lecture 5 - GDA & Naive Bayes

01:18:52

6. Lecture 6 - Support Vector Machines

01:20:57

7. Lecture 7 - Kernels

01:20:25

8. Lecture 8 - Data Splits, Models & Cross-Validation

01:23:26

9. Lecture 9 - Approx_Estimation Error & ERM

01:26:03

10. Lecture 10 - Decision Trees and Ensemble Methods

01:20:41

11. Lecture 11 - Introduction to Neural Networks

01:20:14

12. Lecture 12 - Backprop & Improving Neural Networks

01:16:38

13. Lecture 13 - Debugging ML Models and Error Analysis

01:18:55

14. Lecture 14 - Expectation-Maximization Algorithms

01:20:32

15. Lecture 15 - EM Algorithm & Factor Analysis

01:19:48

16. Lecture 16 - Independent Component Analysis & RL

01:18:10

17. Lecture 17 - MDPs & Value_Policy Iteration

01:19:15

18. Lecture 18 - Continous State MDP & Model Simulation

01:20:15

19. Lecture 19 - Reward Model & Linear Dynamical System

01:21:07

20. Lecture 20 - RL Debugging and Diagnostics

01:12:43

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