Springer The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition

Good for technical person as it is purely mathematical

Great looking book

Livre parfait pour les personnes avec un bon background statistique, sinon je vous recommande pattern recognition and machine learning de Christopher M. Bishop qui repars de la base mais tend à suggérer une pensée plus bayesienne. Lire les deux vous donnera une vision Clair d'un peut prêt tout sur le machine learning hors réseau de neurones.

Questo volume è fondamentale per chiunque voglia approfondire le proprie basi (teoriche...) sull'apprendimento statistico. Scritto dai titani del campo, è un libro omnicomprensivo che, partendo dalle basi (nei primi capitoli, probabilmente per introdurli in maniera strumentale alla trattazione sviluppata, vengono descritte le tecniche base di regressione e classificazione) arriva a descrivere concetti molto più complessi e avanzati, come le varie tecniche di regolarizzazione (Ridge, LASSO), il metodo di Benjamini-Hochberg, le SVM etc.

There is no other book I know of in this space with the same combination of thorough detailed math, intuition, application to real-world data, and excellent graphics. It's also very well-written. Their notation can be a bit weird, but whatever. Maybe I'm weird for finding their notation weird. Enough praise. Just buy it and study it. I personally like it better than the comparable books by Barber, Bishop, Murphy, and others, but to each their own. These three are excellent books in their own right, and maybe some would prefer them, especially if one does a lot of Bayesian modeling. But usually, one doesn't. And if you're a beginner in machine learning, my opinion is that studying Bayesian inference as a default can be confusing. Reading advice, if you're not a mathematician (if you are, you don't need my advice): I highly recommend going through a book on standard statistical inference first, else you might be a bit lost, and subtle points that Hastie et al make might be missed (I often pick up details on a second reading - lots of "aha" moments to be had). Not to mention the fact that some of their derivations will seem impenetrable; that one for bias and variance of the linear model in chapter 2 nonplussed me for a while. Luckily there are the accompanying notes by Weatherwax et al (google it), which are seriously helpful. Good options for background are Casella & Berger (the standard), the book "Statistical rethinking from scratch" by Edge (such a good book!), the book "Probability and mathematical statistics" by Meyer (this looks excellent but I don't know it well), and many others (the number of books written on statistical inference asymptotically approaches infinity). Some people like the book by Wasserman but I find it so "skeletal" (as one reviewer said) that one has to go elsewhere for the details anyway. So why not just read a less skeletal book? Anyway, back to ESL. Reading this has made me a less dumb person, even though I've only read in detail the first 3 chapters. I hope it will do the same for you.