斯坦福大学-机器学习公开课课件.rar

斯坦福大学的机器学习公开课课件 Lecture notes 1 (ps) (pdf) Supervised Learning, Discriminative Algorithms Lecture notes 2 (ps) (pdf) Generative Algorithms Lecture notes 3 (ps) (pdf) Support Vector Machines Lecture notes 4 (ps) (pdf) Learning Theory Lecture notes 5 (ps) (pdf) Regularization and Model Selection Lecture notes 6 (ps) (pdf) Online Learning and the Perceptron Algorithm. (optional reading) Lecture notes 7a (ps) (pdf) Unsupervised Learning, k-means clustering. Lecture notes 7b (ps) (pdf) Mixture of Gaussians Lecture notes 8 (ps) (pdf) The EM Algorithm Lecture notes 9 (ps) (pdf) Factor Analysis Lecture notes 10 (ps) (pdf) Principal Components Analysis Lecture notes 11 (ps) (pdf) Independent Components Analysis Lecture notes 12 (ps) (pdf) Reinforcement Learning and Control Section Notes Section notes 1 (pdf) Linear Algebra Review and Reference Section notes 2 (pdf) Probability Theory Review Files for the Matlab tutorial: sigmoid.m, logistic_grad_ascent.m, matlab_session.m Section notes 4 (ps) (pdf) Convex Optimization Overview, Part I Section notes 5 (ps) (pdf) Convex Optimization Overview, Part II Section notes 6 (ps) (pdf) Hidden Markov Models Section notes 7 (pdf) The Multivariate Gaussian Distribution Section notes 8 (pdf) More on Gaussian Distribution Section notes 9 (pdf) Gaussian Processes