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This question already has an answer here:

What books on machine learning are recommended for a CS graduate student without a huge background in statistics?

I do have some background in ML (and of course linear algebra, probability, etc.) but not really a great/firm background in statistics.

I saw a lot of recommendations for:

  1. PRML: Pattern Recognition and Machine Learning by Bishop.
  2. ESL: Elements of statistical Learning by Hastie et al.
  3. MLaPP: Machine Learning - a Probabilistic Perspective by Murphy.

Which ML book is recommended for a grad student with my background? I see MLaPP is pretty new but seems to include a lot of topics including deep learning etc. - I'd love to know what did someone who read it though of it.

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marked as duplicate by gung, Andy, whuber Nov 14 '14 at 23:28

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

  • $\begingroup$ I don't really see anything terribly distinct about your background. There are a lot of threads on this topic already. Try searching the site & reading some of the existing threads. $\endgroup$ – gung Nov 14 '14 at 22:32
  • $\begingroup$ ESL. Or, if you feel a little rusty re: Math, try their ISLR book. $\endgroup$ – Steve S Nov 15 '14 at 11:17