The Elements of Statistical Learning

Well, it is a textbook -- one doesn't necessarily read a textbook from cover to cover. However, over the years I've certainly covered and re-covered large swathes of it. It is largely irrelevant for deep learning but its still the best reference I know of for the classic methods.

The Elements of Statistical Learning

Second that. Great to have a no-nonsense textbook in the space.

The Elements of Statistical Learning. This book features some of the most interesting applications of mathematics. It was the likely reason why I started enjoying maths after many years of studying it. https://hastie.su.domains/Papers/ESLII.pdf

I'm coming from an applied math perspective. A few of my favorites (and ones I find myself regularly referring to) are: Matrix Analysis by Horn and Johnson (perhaps the best end-of-chapter problem sets of any math book I've encountered!) Matrix Computations by Golub and Van Loan Elements of Statistical Learning by Hastie, Friedman, Tibshirani Functional Analysis by Reed and Simon

Attempting to self study statistics and econometrics in a serious way. I just finished Casella and Berger's "Statistical Inference". I'm about halfway through Shumway and Stoffer's "Time Series Analysis", but then next on the list, in the order I tend to read them: - Theory of Point Estimation Casella/Lehmann - Bayesian Data Analysis 3rd ed. - Convex Optimization - Boyd - Econometrics - Hayashi - New Introduction to Multiple Time Series Analysis - Elements of Statistical Learning - Machine Learning: A probabilistic perspective There's a fair amount of overlap between these books, so it's not quite as much as it seems. But i'm hoping to make it through at least a chapter a week this year, which should get me most of the way through them. We'll see how it goes.

This for the background: https://mml-book.github.io/ This for actual methods: http://www.deeplearningbook.org/ This is also useful, but harder to read than the previous ones: https://web.stanford.edu/~hastie/Papers/ESLII.pdf

The Elements of Statistical Learning. (https://web.stanford.edu/~hastie/Papers/ESLII.pdf)

The Elements of Statistical Learning, by Hastie, Tibshirani and Friedman, for everything on Machine Learning and Statistics. Available for free online: https://statweb.stanford.edu/~tibs/ElemStatLearn/

For applications to machine learning, I strongly recommend Elements of Statistical Learning. The text is eye-opening. A PDF is freely available on Rob Tibshirani's website: http://www-stat.stanford.edu/~tibs/ElemStatLearn/

Lately I've been staring at the Codex Seriphinianus quite a bit. Worth finding a copy if you haven't seen it before. From a technical point of view, The Elements of Statistical Learning, by Tibshirani, Friedman, and Hastie. Far and away the most illuminating demonstration that so many ML / AI techniques have a long-standing statistical foundation, and, essentially, everything boils down to the linear model.

Don't forget Hastie & Tibshirani's Elements of Statistical Learning! :)

I'll second this recommendation - I bought the printed copy, and I'm constantly going to it for reference. The fact that it's available for free is just an added bonus. I would also suggest Elements of Statistical Learning: http://www-stat.stanford.edu/~tibs/ElemStatLearn/ As well as Duda, Hart, and Stork's Pattern Classification: http://rii.ricoh.com/~stork/DHS.html