文件名称：斯坦福大学-机器学习公开课课件.rar
文件大小：3.32MB
文件格式：RAR
更新时间：2017-06-12 08:47:07
斯坦福 机器学习 课件
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
【文件预览】：
课件
----Section notes 5-Convex Optimization Overview, Part II .pdf(197KB)
----cs229-notes4 Learning Theory .pdf(109KB)
----cs229-notes5 Regularization and Model Selection .pdf(87KB)
----cs229-notes3 Support Vector Machines .pdf(176KB)
----Section notes 6-Hidden Markov Models .pdf(198KB)
----Section notes 4-Convex Optimization Overview, Part I.pdf(149KB)
----cs229-notes9 Factor Analysis .pdf(81KB)
----cs229-notes12 Reinforcement Learning and Control .pdf(74KB)
----cs229-prob Probability Theory Review .pdf(147KB)
----cs229-notes7a Unsupervised Learning, k-means clustering. .pdf(265KB)
----cs229-notes11 Independent Components Analysis .pdf(74KB)
----cs229-notes2 Generative Algorithms .pdf(858KB)
----cs229-notes10 Principal Components Analysis .pdf(75KB)
----cs229-notes8 The EM Algorithm .pdf(81KB)
----Section notes 1-Linear Algebra Review and Reference .pdf(165KB)
----cs229-notes1 Supervised Learning, Discriminative Algorithms .pdf(230KB)
----cs229-notes6 Online Learning and the Perceptron Algorithm. (optional reading).pdf(51KB)
----Section notes 9-Gaussian Processes.pdf(160KB)
----cs229-notes7b Mixture of Gaussians .pdf(54KB)
----ML-advice.pdf(313KB)
----Section notes 7-The Multivariate Gaussian Distribution.pdf(335KB)
----Section notes 8-More on Gaussian Distribution.pdf(117KB)