[wodenokoto]

“ An Introduction to Statistical Learning” even if it is with examples in R and you don’t care about R.

It’s also not aimed at complete beginners, but if you have a very solid grasp on high school math and some blood on your teeth, you can totally do it.

There’s also a coursera course and the book is free as a pdf

[jstx1]

ISLR is a great introduction to machine learning, not necessarily to statistics. It doesn't cover stuff like hypothesis testing or simulation methods.

[melling]

What should one read after ISLR?

Could probably go through it again but I’d also like more of a theoretical background.

Isn’t OLS a specific case of MLE, for example ?

Anyway, I’d like to understand machine learning and statistical inference more deeply.

I’d be happy to get the answers by reading a handful of books.

[jstx1]

Elements of Statistical learning is from the same authors (mostly) as ISLR but it's longer and goes a bit deeper on the theory. Also, Computer Age Statistical Inference which is newer and covers more material.

Other big titles - Pattern Recognition and Machine Learning (Bishop), Machine Learning: A Probabilistic Perspective (Murphy) - these last two weren't for me but different people respond to different approaches so check them out especially if the first two books don't click for you.

[wodenokoto]

> Also, Computer Age Statistical Inference which is newer and covers more material.

isn't that more a history of modern statistics than an actual statistics textbook?

[jstx1]

Not at all, take a look - https://hastie.su.domains/CASI_files/PDF/casi.pdf