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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:
- PRML: Pattern Recognition and Machine Learning by Bishop.
- ESL: Elements of statistical Learning by Hastie et al.
- 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.