CS229 机器学习：数学直觉与算法

本笔记按章节拆分为子页，逐章阅读更快。

斯坦福 CS229 Machine Learning 系统学习笔记，涵盖监督学习、生成学习、核方法、深度学习、学习理论与强化学习。

章节

• 开篇与课程概览
• Lecture 1: Introduction
• Lecture 2: Supervised Learning Setup. LMS（线性回归与最小均方）
• Lecture 3: Weighted Least Squares. Logistic Regression. Newton’s Method
• Lecture 4: Dataset Split; Exponential Family. Generalized Linear Models (GLMs)
• Lecture 5: Bias-Variance Tradeoff. Regularization
• Lecture 6: Gaussian Discriminant Analysis. Naive Bayes, Laplace Smoothing
• Lecture 7: Kernels. Support Vector Machines (SVM)
• Lecture 8: K-Means. Gaussian Mixture Models (GMM). Expectation Maximization (EM)
• Lecture 9: Decision Trees
• Lecture 10: Boosting（AdaBoost）
• Lecture 11: Neural Networks 1（前向传播与基础架构）
• Lecture 12: Neural Networks 2（反向传播 Backpropagation）
• Lecture 13: ML Advice（调试机器学习系统）
• Lecture 14: Basic Concepts in RL. Value Iteration. Policy Iteration
• Lecture 15: Model-based RL. Value Function Approximation
• Lecture 16: Principal Components Analysis (PCA)
• Lecture 17: Large Language Models — Learning Tasks, Language Modeling, Embeddings, Transformers
• Lecture 18: Large Language Models — RAG, Fine-tuning, Prompt Optimization, Safety
• Lecture 19: Fairness, Algorithmic Bias（公平性与算法偏见）
• Lecture 20: Explainability, Privacy（可解释性与隐私）
• 6. 核心数学与算法速查表（Quick Reference）