Andrew NG CS229 机器学习课程笔记（Lecture，Section 和Extra完整版） Stanford CS229 Machine Learning Class Notes http://cs229.stanford.edu/materials.html 第一部分：Lecture Notes （cs229-notes-all.pdf） Lecture notes 1 Supervised Learning, Discriminative Algorithms Lecture notes 2 Generative Algorithms Lecture notes 3 Support Vector Machines Lecture notes 4 Learning Theory Lecture notes 5 Regularization and Model Selection Lecture notes 6 Online Learning and the Perceptron Algorithm. Lecture notes 7a Unsupervised Learning, K-means clustering. Lecture notes 7b Mixture of Gaussians Lecture notes 8 The EM Algorithm Lecture notes 9 Factor Analysis Lecture notes 10 Principal Components Analysis Lecture notes 11 Independent Components Analysis Lecture notes 12 Reinforcement Learning and Control 第二部分：Section Notes （cs229-section-all.pdf） Section notes 1 Linear Algebra Review and Reference Section notes 2 Probability Theory Review Section notes 3 Files for the Matlab tutorial Section notes 4 Convex Optimization Overview, Part I Section notes 5 Convex Optimization Overview, Part II Section notes 6 Hidden Markov Models Section notes 7 The Multivariate Gaussian Distribution Section notes 8 More on Gaussian Distribution Section notes 9 Gaussian Processes 第三部分：Extra Notes （cs229-extra-all.pdf） Supplemental notes 1 Binary classification with +/-1 labels. Supplemental notes 2 Boosting algorithms and weak learning. Supplemental notes 3 The representer theorem. Supplemental notes 4 Hoeffding's inequality. 版权归斯坦福大学所有。 感谢Andrew NG教授的精彩课程！