Following are the texts to be used to guide the projects during the sessions. Purchasing these are optional but encouraged, to have them as a reference:

Machine Learning in Action
Peter Harrington
Manning Publications Co. Greenwich, CT
April 2012
ISBN 9781617290183
384 pages
Barnes and Noble
Manning (eBook)
Source code

Real-World Machine Learning
Henrik Brink, Joseph W. Richards, and Mark Fetherolf
Manning Publications Co. Greenwich, CT
Publication in July 2016 (estimated)
ISBN 9781617291920
400 pages (estimated)
source code


CU Boulder’s Prof. Jordan Boyd-Graber offers courses in Data Science. Consider enrolling in a degree program there, here are some textbooks from those courses providing more in-depth background in Machine Intelligence:

The following texts will be used as the basis for many of the presentations and activities in this meetup.

Machine Learning: A Probabilistic Perspective
Kevin P. Murphy
The MIT Press
Cambridge, Massachusetts
August 24, 2012
ISBN 0262018020
1104 pages

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
Trevor Hastie
Robert Tibshirani
Jerome Friedman
February 2009
ISBN: 978-0387848570
745 pages
(available as free download)

Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
The MIT Press
Cambridge, Massachusetts
ISBN 9780262018258
432 pages

Tammi donated these links: