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斯坦福CS229马腾宇 - 统计学习理论天花板（机器学习）

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2026-05-16 17:17:41

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资料可在gong众号【Abner说Ai】回复【12】即可无偿获取~ 本课程提供机器学习和统计模式识别的广泛入门介绍。主题包括：监督式学习（生成式/判别式学习、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、核法）;学习理论（偏误/方差权衡，实用建议）;强化学习与自适应控制。课程还将讨论机器学习的最新应用，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网络数据处理。

AI算法实战营发消息

分享 AI 算法实战教程：从基础代码到模型搭建、论文复现，手把手带练深度学习全流程～

Lecture 1 Machine Learning Introduction

01:18:42

Lecture 2 Supervised learning setup, LMS

59:56

Lecture 3 Weighted Least Squares, Logistic regression, Newton's Method

01:12:59

Lecture 4 Exponential family, Generalized Linear Models

01:17:25

Lecture 5 Gaussian discriminant analysis, Naive Bayes

01:28:34

Lecture 6 Naive Bayes, Laplace Smoothing

01:23:24

Lecture 7 Kernels

01:28:24

Lecture 8 Neural Networks 1

01:10:16

Lecture 9 Neural Networks 2 (backprop)

01:29:32

Lecture 10 Bias - Variance, Regularization

01:30:23

Lecture 11 Feature _ Model selection, ML Advice

01:29:10

Lecture 12 K-Means, GMM (non EM), Expectation Maximization

01:26:40

Lecture 13 GMM (EM)

01:27:52

Lecture 14 Factor Analysis_PCA

01:29:14

Lecture 15 PCA_ICA

01:16:48

Lecture 16 Self-supervised learning

01:23:03

Lecture 17 Basic concepts in RL, Value iteration, Policy iteration

01:30:43

Lecture 18 Societal impact of ML (Guest lecture by Prof. James Zou)

59:51

Lecture 20 Model-based RL, Value function approximator

01:20:22

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