Machine Learning
28 Oct 2017
Machine Learning


  1. Machine Learning: A Probabilistic Perspective by Kevin Murphy, 2012. Great book!
  2. Pattern Recognition and Machine Learning by Christopher Bishop, 2011. Good all round book.
  3. Elements of Statistical Learning (free online PDF) by Hastie, Tibshirani and Friedman.
  4. Probabilistic Graphical Models by Koller and Friedman, 2009.
  5. 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.
  6. Machine Learning by Tom Mitchell, 1997. Good for theory behind ML.


  1. Andrew Ng's Course on Machine Learning: CourseraVideosStanford VideosLinear Algebra Prerequisite Notes by Chuong Do.


  1. A few useful things to know about machine learning", Pedro Domingos, CACM 2012.

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