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href="http://bigtop.apache.org/";>Apache Bigtop</a></li> + <li><a href="http://spark.apache.org/";>Apache Spark</a></li> + <li><a href="http://flink.apache.org/";>Apache Flink</a></li> + </ul> + </div> +</div> + + <div id="content-wrap" class="clearfix"> + <div id="main"> + + <h1 id="reference-reading">Reference Reading</h1> + +<p>Here we provide references to books and courses about data analysis in general, which might also be helpful in the context of Mahout.</p> + +<p><a name="ReferenceReading-GeneralBackgroundMaterials"></a></p> +<h2 id="general-background-materials">General Background Materials</h2> + +<p>Donât be overwhelmed by all the maths, you can do a lot in Mahout with some +basic knowledge. The books will help you understand your +data better, and ask better questions both of Mahoutâs APIs, and also of +the Mahout community. And unlike learning some particular software tool, +these are skills that will remain useful decades later.</p> + +<ul> + <li><a href="http://www-math.mit.edu/~gs";>Gilbert Strang</a> +âs <a href="http://math.mit.edu/linearalgebra/";>Introduction to Linear Algebra</a>. His <a href="http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/";>lectures</a> are also <a href="http://web.mit.edu/18.06/www/";>available online</a> + and are strongly recommended.</li> + <li><a href="http://www.amazon.com/Mathematical-Tools-Applied-Multivariate-Analysis/dp/0121609553/ref=sr_1_1?ie=UTF8&qid=1299602805&sr=8-1";>Mathematical Tools for Applied Mulitvariate Analysis</a> by J.Douglass +Carroll.</li> + <li><a href="http://www.stanford.edu/class/cs229/";>Stanford Machine Learning online courseware</a></li> + <li><a href="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/";>MIT Machine Learning online courseware</a> has <a href="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/";>lecture notes</a> online.</li> + <li>As a pre-requisite to probability and statistics, youâll need <a href="http://en.wikipedia.org/wiki/Calculus";>basic calculus</a>. A maths for scientists text might be useful here such as âMathematics for Engineers and Scientistsâ, Alan Jeffrey, Chapman & Hall/CRC. (<a href="http://openlibrary.org/books/OL3305993M/Mathematics_for_engineers_and_scientists";>openlibrary</a>)</li> + <li>One of the best writers in the probability/statistics world is Sheldon Ross. Try <a href="http://www.pearsonhighered.com/educator/product/First-Course-in-Probability-A/9780136033134.page";>A First Course in Probability (8th Edition)</a> and then move on to his <a href="http://www.amazon.com/Introduction-Probability-Models-Sixth-Sheldon/dp/0125984707";>Introduction to Probability Models</a></li> +</ul> + +<p>Some good introductory alternatives here are:</p> + +<ul> + <li><a href="http://www.khanacademy.org/";>Kahn Academy</a> â videos on stats, probability, linear algebra</li> + <li><a href="http://www.amazon.com/Probability-Statistics-Engineering-Sciences-InfoTrac/dp/0534399339";>Probability and Statistics (7th Edition)</a>, Jay L. Devore, Chapman.</li> + <li><a href="http://www.amazon.com/Probability-Statistical-Inference-Robert-Hogg/dp/0132546086";>Probability and Statistical Inference (7th Edition)</a>, Hogg and Tanis, Pearson.</li> +</ul> + +<p>Once you have a grasp of the basics then there are a slew of great texts that you might consult:</p> + +<ul> + <li><a href="http://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126";>Statistical Inference</a>, Casell and Berger, Duxbury/Thomson Learning.</li> + <li><a href="http://www.amazon.com/Introduction-Bayesian-Statistics-William-Bolstad/dp/0471270202";>Introduction to Bayesian Statistics</a>, William H. Bolstad, Wiley.</li> + <li><a href="http://www.amazon.com/Understanding-Computational-Bayesian-Statistics-Wiley/dp/0470046090";>Understanding Computational Bayesian Statistics</a>, Bolstadt</li> + <li><a href="http://www.stat.columbia.edu/~gelman/book/";>Bayesian Data Analysis, Gelman et al.</a></li> +</ul> + +<h2 id="for-statistics-related-to-machine-learning-these-are-particularly-helpful">For statistics related to machine learning, these are particularly helpful:</h2> + +<ul> + <li><a href="http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm";>Pattern Recognition and Machine Learning by Chris Bishop</a></li> + <li><a href="http://www-stat.stanford.edu/~tibs/ElemStatLearn/";>Elements of Statistical Learning</a> by Trevor Hastie, Robert Tibshirani, Jerome Friedman</li> + <li><a href="http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm";>http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm</a></li> +</ul> + +<h2 id="for-matrix-computationsdecompositionfactorization-etc">For matrix computations/decomposition/factorization etc.:</h2> + +<ul> + <li>Peter V. OâNeil <a href="http://www.amazon.com/Introduction-Linear-Algebra-Theory-Applications/dp/053400606X";>Introduction to Linear Algebra</a>, great book for beginners (with some knowledge in calculus). It is not comprehensive, but, it will be a good place to start and the author starts by explaining the concepts with regards to vector spaces which I found to be a more natural way of explaining.</li> + <li>David S. Watkins <a href="http://www.amazon.com/Fundamentals-Matrix-Computations-Applied-Mathematics/dp/0470528338/";>Fundamentals of Matrix Computations</a></li> + <li><a href="http://www.amazon.com/Computations-Hopkins-Studies-Mathematical-Sciences/dp/0801854148/ref=sr_1_2?s=books&ie=UTF8&qid=1394307676&sr=1-2&keywords=golub+van+loan";>Matrix Computations</a> is the classic text for numerical linear algebra. Canât go wrong with it - great for researchers.</li> + <li>Nick Trefethenâs <a href="http://people.maths.ox.ac.uk/trefethen/books.html";>Numerical Linear Algebra</a>. Itâs a bit more approachable for practitioners. Many chapters on SVD, there are even chapters on Lanczos.</li> +</ul> + +<h2 id="books-specifically-on-r">Books specifically on R:</h2> + +<ul> + <li>Learning about R is a difficult thing. The best introduction is in MASS <a href="http://www.stats.ox.ac.uk/pub/MASS4/";>http://www.stats.ox.ac.uk/pub/MASS4/</a></li> + <li><a href="http://www.r-tutor.com/r-introduction";>R Tutor</a></li> + <li><a href="http://cran.r-project.org/doc/manuals/R-intro.pdf";>Manual</a></li> + <li><a href="http://faculty.washington.edu/tlumley/Rcourse/";>R Course</a></li> +</ul> + +<p>In addition, you should see how to plot data well:</p> + +<ul> + <li><a href="http://www.statmethods.net/advgraphs/trellis.html";>Trellis plotting</a></li> + <li><a href="http://had.co.nz/ggplot2/";>ggplot2</a></li> +</ul> + + + </div> + </div> +</div> + <footer class="footer" align="center"> + <div class="container"> + <p> + Copyright © 2014-2016 The Apache Software Foundation, Licensed under + the <a href="http://www.apache.org/licenses/LICENSE-2.0";>Apache License, Version 2.0</a>. + <br /> + Apache Mahout, Mahout, Apache, the Apache feather logo, and the elephant rider logo are either registered trademarks or trademarks of <a href="http://www.apache.org/foundation/marks/";>The Apache Software Foundation</a> in the United States and other countries. + </p> + </div> + </footer> + + <script src="/assets/themes/mahout-retro/js/jquery-1.9.1.min.js"></script> + <script src="/assets/themes/mahout-retro/js/bootstrap.min.js"></script> + <script> + (function() { + var cx = '012254517474945470291:vhsfv7eokdc'; + var gcse = document.createElement('script'); + gcse.type = 'text/javascript'; + gcse.async = true; + gcse.src = (document.location.protocol == 'https:' ? 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