Machine Learning Repository Interactive Statistical Calculation Pages CS 229: Machine Learning (Course handouts) 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 Supplemental notes 1 (pdf) Binary classification with +/-1 labels. Supplemental notes 2 (pdf) Boosting algorithms and weak learning.
Resources for Statistical Computing Other Resources for Help with Statistical Computing The primary mission of the IDRE Statistical Consulting Group is to support UCLA researchers in statistical computing using statistical packages like SAS, Stata, SPSS, HLM, MLwiN, Mplus and so forth. We provide this support through our web pages, our walk in consulting services, classes and seminars, and email consulting. Below, we provide a list of commonly used statistical software packages along with sources of support, including newsgroups/mailing lists, web pages provided by the vendors, and the vendor's technical support email address. Other lists news:sci.stat.consult - General issues in statistics. The content of this web site should not be construed as an endorsement of any particular web site, book, or software product by the University of California.
Mining of Massive Datasets The book has now been published by Cambridge University Press. The publisher is offering a 20% discount to anyone who buys the hardcopy Here. By agreement with the publisher, you can still download it free from this page. --- Jure Leskovec, Anand Rajaraman (@anand_raj), and Jeff Ullman Download Version 2.1 The following is the second edition of the book, which we expect to be published soon. There is a revised Chapter 2 that treats map-reduce programming in a manner closer to how it is used in practice, rather than how it was described in the original paper. Version 2.1 adds Section 10.5 on finding overlapping communities in social graphs. Download the Latest Book (511 pages, approximately 3MB) Download chapters of the book: Download Version 1.0 The following materials are equivalent to the published book, with errata corrected to July 4, 2012. Download the Book as Published (340 pages, approximately 2MB) Gradiance Support Other Stuff Jure's Materials from the most recent CS246.