【ML】斯坦福CS229：机器学习中英文字幕 by Andrew Ng
正在缓冲...
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。
视频选集
（1/20）
自动连播
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
顶部