Machine Learning: A Probabilistic Perspective
by Kevin Murphy, 2012. Great book!
Pattern Recognition and Machine Learning
by Christopher Bishop, 2011. Good all round book.
Elements of Statistical Learning
(free online PDF) by Hastie, Tibshirani and Friedman.
Probabilistic Graphical Models
by Koller and Friedman, 2009.
Mining of Massive Datasets
by Leskovec, Rajaraman and Ullman, 2014. 12 chapters, 480 pages. Not an ML book but good for various techniques for handling massive datasets.
by Tom Mitchell, 1997. Good for theory behind ML.
Andrew Ng's Course on Machine Learning:
Linear Algebra Prerequisite Notes
by Chuong Do.
A few useful things to know about machine learning"
, Pedro Domingos, CACM 2012.
28 Oct 2017
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