![]() Then drag it into iBooks (under the section: PDF files). orĭownload the entire book as a single PDF file with live clickable Table of Contents etc. The entire book as a single download file. even as a reference text for people who do not even have Mathematica. Although no software is included (neither Mathematica nor mathStatica), the methods, theorems, summary tables, examples, exercises, theorems etc are all useful and relevant. This is a complete PDF version of the original 2002 printed edition. We are delighted to be able to make the PDF eBook version (2002 edition) available for FREE to stackexchange users at: The printed version of our 2002 edition was printed 3 times and sold out 3 times Springer and Google recently started selling it (book only) as a PDF eBook (no software) on the Springer and Google sites for $79. I know other authors have gone to some trouble to make their books available here on stack exchange. Propagation of Error, and Standard Errors for Derived QuantitiesĬhi-squared and the Likelihood Ratio Test Relative Distributions and Smooth Tests of Goodness-of-Fit Generalized Linear Models and Generalized Additive Models A classical, much more detailed but a bit dated textbook is Sutton & Barto, Reinforcement Learning: An Introduction which is also freely available online but only in a cumbersome HTML format.īoyd and Vandenberghe, Convex Optimization.Ĭosma Shalizi, CMUs ML guru, occasionally updates a draft of a stats book soon to be published by Cambridge Press titled Advanced Data Analysis from an Elementary Point of View. here on github.Ĭsaba Szepesvári, Algorithms for Reinforcement Learning, a concise book on RL. On the official website one can view it in browser but cannot download (as per agreement with the publisher), but it is easy to find a combined PDF e.g. It isn't published yet, but the book is already available online. Much awaited Goodfellow, Bengio and Courville Deep Learning textbook that is about to be published by MIT Press. Rasmussen & Williams Gaussian Processes for Machine Learning, which is the book on Gaussian processes. Switching now to more specialized topics, there are: I did not use it myself, but it is widely considered to be an excellent book. This one is quite a bit shorter and focuses specifically on lasso.Īnother freely available all-encompassing machine learning textbook is David Barber's Bayesian Reasoning and Machine Learning. In 2015, Hastie & Tibshirani co-authored a new textbook Statistical Learning with Sparsity: The Lasso and Generalizations, also available online. Hastie & Tibshirani also co-wrote freely available An Introduction to Statistical Learning, With Applications in R which is basically a simpler version of The Elements and focuses on R. ![]() to Bishop's Pattern Recognition and ML or Murphy's ML, but those books are not free, while ESL is. One the most, if not the most, popular textbooks on machine learning is Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning, which is fully available online (currently 10th printing).
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