Books

1. Machine Learning: a Probabilistic Perspective, by Kevin P. Murphy, MIT Press
   Amazon link, Downloaded
   TODO: Work out the examples in it and also could be used as seed for getting the other papers & books
   Google cites of the above book: 80 as on Sep 2013
2. Boosting - Foundations & Algorithms, By Robert E. Schapire and Yoav Freund, MIT Press, May 2012. Downloaded
3. Pattern Classification Using Ensemble Methods, 2010, Only 2-stars.
   ACM cites - 12, Google cites - 56
4. Ensemble Machine Learning: Methods and Applications, by Cha Zhang, Yunqian Ma. Springer Feb 2012
5. Machine Learning: The Art and Science of Algorithms that Make Sense of Data|, CUP, Sep 2012
6. All of Statistics: A concise course in statistical inference,Amazon link, Oct 2004
   Book site
7. Time series analysis and its applications: with R examples, 3rd Ed., Springer
   Amazon link, Downloaded
8. Time series analysis with applications in R, 2nd Ed., Springer, Downloaded
9. Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R, Sep 2013
10. Statistical Analysis of Network Data: Methods and Models,Book web site
11. Associative Neural Memories: Theory & Implementation, by Mohamad H. Hassoun, 1993
12. Neural networks and their applications, by Unicom, 1996
13. Associative processing and processors, by Anargyros Krikelis, Chip Weems,July 1997
14. Neural Computation in Hopfield Networks and Boltzmann Machines, by James P. Coughlin, Robert H. Baran, Jan 1995
15. Neural Networks for Knowledge Representation and Inference, by Daniel S. Levine, Manuel Aparicio, 1994
16. Cerebellar cortex: cytology and organization, by Sanford L. Palay, Victoria Chan-Palay, Springer 1974
17. Local cortical circuits: an electrophysiological study, by Moshe Abeles,Springer, 1982
18. Probabilistic Models of the Brain: Perception and Neural Function, Mar 2002, MIT Press link
19. The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani and Jerome Friedman
20. An Introduction to Statistical Learning with Applications in R, Book Home Page, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
21. Pattern recognition & Machine learning (PRML), by Christopher Bishop, Oct 2007
22. Reinforcement Learning, by Sutton, Mar 1998.
    Old book, not sure how good it is!!
23. Probabilistic Robotics, by Sebastian Thurn, Aug 2005
24. Markov Random Fields for Vision and Image Processing, MIT Press, Jul 2011.
    Downloaded.
25. Bayesian Reasoning and Machine Learning, by David Baker, CUP, 2012
    Downloaded
26. Graphical Models with R (Use R!), Springer, Feb 2012
    Download link, Downloaded. Notes link
27. Probabilistic Graphical Models - Principles and Techniques, by Daphne Koller, N.Freidman
    MIT Press link, Nov 2009, 1208 pages.
    Downloaded
28. Scaling up Machine Learning: Parallel and Distributed Approaches, edited by Ron Bekkerman (LinkedIn Corporation), Mikhail Bilenko (Microsoft Research), John Langford (Yahoo! Research), Dec 2011, CUP.