[mindviews]

For Machine Learning, I found "Learning From Data" http://amlbook.com/ to be a very strong, foundational book. It does the best job of any reference I've seen of answering the question: "Is learning possible and feasible?" Provides and excellent mathematical foundation for understanding when and why ML systems should succeed and fail. Associated MOOC from Caltech at https://work.caltech.edu/telecourse.html

[saamm]

My ML professor used this book, and I loved it. Some presentations of ML make it seem like a bag of tricks; this feels like the opposite of that.

[Eridrus]

I'm a big fan of the MOOC, even though it didn't explicitly cover it, it basically explained what people mean when they say "we don't know why neural nets work", which is that our previous generalization bounds would imply that neural nets would not generalize well, but in practice they do.