Reference Reading

Here we provide references to books and courses about data analysis in general, which might also be helpful in the context of Mahout.

General Background Materials

Don’t 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 Mahout’s APIs, and also of the Mahout community. And unlike learning some particular software tool, these are skills that will remain useful decades later.

• Gilbert Strang ‘s Introduction to Linear Algebra. His lectures are also available online and are strongly recommended.
• Mathematical Tools for Applied Mulitvariate Analysis by J.Douglass Carroll.
• Stanford Machine Learning online courseware
• MIT Machine Learning online courseware has lecture notes online.
• As a pre-requisite to probability and statistics, you’ll need basic calculus. A maths for scientists text might be useful here such as ‘Mathematics for Engineers and Scientists’, Alan Jeffrey, Chapman & Hall/CRC. (openlibrary)
• One of the best writers in the probability/statistics world is Sheldon Ross. Try A First Course in Probability (8th Edition) and then move on to his Introduction to Probability Models

Some good introductory alternatives here are:

• Kahn Academy – videos on stats, probability, linear algebra
• Probability and Statistics (7th Edition), Jay L. Devore, Chapman.
• Probability and Statistical Inference (7th Edition), Hogg and Tanis, Pearson.

Once you have a grasp of the basics then there are a slew of great texts that you might consult:

• Statistical Inference, Casell and Berger, Duxbury/Thomson Learning.
• Introduction to Bayesian Statistics, William H. Bolstad, Wiley.
• Understanding Computational Bayesian Statistics, Bolstadt
• Bayesian Data Analysis, Gelman et al.

For statistics related to machine learning, these are particularly helpful:

• Pattern Recognition and Machine Learning by Chris Bishop
• Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman
• http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm

For matrix computations/decomposition/factorization etc.:

• Peter V. O’Neil Introduction to Linear Algebra, 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.
• David S. Watkins Fundamentals of Matrix Computations
• Matrix Computations is the classic text for numerical linear algebra. Can’t go wrong with it - great for researchers.
• Nick Trefethen’s Numerical Linear Algebra. It’s a bit more approachable for practitioners. Many chapters on SVD, there are even chapters on Lanczos.

Books specifically on R:

• Learning about R is a difficult thing. The best introduction is in MASS http://www.stats.ox.ac.uk/pub/MASS4/
• R Tutor
• Manual
• R Course

In addition, you should see how to plot data well:

• Trellis plotting
• ggplot2

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