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The following is a list of free, open source books on machine learning, statistics, data-mining, etc.
## Machine-Learning / Data Mining
* [An Introduction To Statistical Learning ](http://www-bcf.usc.edu/~gareth/ISL/ ) - Book + R Code
* [Elements of Statistical Learning ](http://statweb.stanford.edu/~tibs/ElemStatLearn/ ) - Book
* [Probabilistic Programming & Bayesian Methods for Hackers ](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/ ) - Book + IPython Notebooks
* [Thinking Bayes ](http://www.greenteapress.com/thinkbayes/ ) - Book + Python Code
* [Information Theory, Inference, and Learning Algorithms ](http://www.inference.phy.cam.ac.uk/mackay/itila/book.html )
* [Gaussian Processes for Machine Learning ](http://www.gaussianprocess.org/gpml/chapters/ )
* [Data Intensive Text Processing w/ MapReduce ](http://lintool.github.io/MapReduceAlgorithms/ )
* [Reinforcement Learning: - An Introduction ](http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html )
* [Mining Massive Datasets ](http://infolab.stanford.edu/~ullman/mmds/book.pdf )
* [A First Encounter with Machine Learning ](https://www.ics.uci.edu/~welling/teaching/273ASpring10/IntroMLBook.pdf )
* [Pattern Recognition and Machine Learning ](http://www.hua.edu.vn/khoa/fita/wp-content/uploads/2013/08/Pattern-Recognition-and-Machine-Learning-Christophe-M-Bishop.pdf )
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* [Machine Learning & Bayesian Reasoning ](http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf )
* [Introduction to Machine Learning ](http://alex.smola.org/drafts/thebook.pdf )
* [A Probabilistic Theory of Pattern Recognition ](http://www.szit.bme.hu/~gyorfi/pbook.pdf )
* [Introduction to Information Retrieval ](http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf )
* [Forecasting: principles and practice ](http://otexts.com/fpp/ )
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* [Practical Artificial Intelligence Programming in Java ](http://www.markwatson.com/opencontent_data/JavaAI3rd.pdf )
* [Introduction to Machine Learning ](http://arxiv.org/pdf/0904.3664v1.pdf )
* [Reinforcement Learning ](http://www.intechopen.com/books/reinforcement_learning )
* [Machine Learning ](http://www.intechopen.com/books/machine_learning )
* [A Quest for AI ](http://ai.stanford.edu/~nilsson/QAI/qai.pdf )
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## Probability & Statistics
* [Thinking Stats ](http://www.greenteapress.com/thinkstats/ ) - Book + Python Code
* [From Algorithms to Z-Scores ](http://heather.cs.ucdavis.edu/probstatbook ) - Book
* [The Art of R Programming ](http://heather.cs.ucdavis.edu/~matloff/132/NSPpart.pdf ) - Book (Not Finished)
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* [All of Statistics ](http://www.ucl.ac.uk/~rmjbale/Stat/wasserman2.pdf )
* [Introduction to statistical thought ](https://www.math.umass.edu/~lavine/Book/book.pdf )
* [Basic Probability Theory ](http://www.math.uiuc.edu/~r-ash/BPT/BPT.pdf )
* [Introduction to probability ](http://math.dartmouth.edu/~prob/prob/prob.pdf )
* [Principle of Uncertainty ](http://uncertainty.stat.cmu.edu/wp-content/uploads/2011/05/principles-of-uncertainty.pdf )
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## Linear Algebra
* [Linear Algebra Done Wrong ](http://www.math.brown.edu/~treil/papers/LADW/LADW.pdf )
* [Linear Algebra, Theory, and Applications ](https://math.byu.edu/~klkuttle/Linearalgebra.pdf )
* [Convex Optimization ](http://www.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf )
* [Applied Numerical Computing ](http://www.seas.ucla.edu/~vandenbe/103/reader.pdf )
* [Applied Numerical Linear Algebra ](http://uqu.edu.sa/files2/tiny_mce/plugins/filemanager/files/4281667/hamdy/hamdy1/cgfvnv/hamdy2/h1/h2/h3/h4/h5/h6/Applied%20Numerical%20Linear%20.pdf )
