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Besides the DL book, there's other excellent texts that are freely available/open content on the web:

- Elements statistical Learning, Hastie et al

- Shalev-Shwartz and Ben-David: http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...

- the late David MacKay's Info Theory

- Bayesian Reasoning in ML, Barber

- Hopcroft/Kannan (this is an older version, you can google latest: http://www.cs.cornell.edu/jeh/book112013.pdf




I didn't expect to see Hopcroft/Kannan in that list. I used it in a course taught by Kannan and back then it was called "Computer Science Theory for the Information Age". Apparently the book is now called "Foundations of Data Science". I always thought the old naming was terrible but compared to the new title it actually describes the content a lot better since the book is mainly about CS theory and mathematical foundations for them. It was one of my favorite courses but I would not classify it as ML book.

[1]https://www.cs.cmu.edu/~venkatg/teaching/CStheory-infoage/

[2]https://www.cs.cornell.edu/jeh/book2016June9.pdf




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