The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book has been an excellent read throughout my PhD and have relied on it heavily.

Some context first: I'm studying my fourth year in a computer engineering program, having studied lightweight mathematics courses only, which is basically calculus, linear algebra, discrete mathematics and matematical statistics. Our machine learning course has two recommended literatures of which "The Elements of Statistical Learning" (ESL) was one of them, while the primary was Pattern Recognition and Machine Learning (PRML). My experience with the book so far if very positive. It contains incredibly relevant machine learning methods/tools which many other books, most notably PRML, doesn't touch upon or at least explain very shortly, which are extensively used in practice. Most notably: Support Vector Machines, Random Forests and Ensemble Learning. Also, the structure of ESL has made a lot more sense to me compared to PRML, it wraps parts of the field into more easily digestible chunks, and therefore makes for a better reference than PRML (just compare the table of contents). Also, as the authors themselves point out, the book itself will rather the reader understands the intuition, algorithm and the cases in which they perform good/bad rather than the mathematical background/proofs behind them (don't worry, most of them are still presented in ESL though). In conclusion, if you can accept the skimming of proof and some rigour in ESL, this book is perfect, and summarizes a large part of the field in such a way that even a mathematically mediocre computer scientist is able to somewhat grasp and apply in real world problems. However, if you want to get the entire picture, you might want to read both ESL and PRML, which will give you some of that Bayesian goodies as well.

Having completed the Coursera Stanford Machine Learning course I wanted to know more and this came up at the top recommended book in Amazon for ML. I downloaded the free PDF but it's huge and I find it impossible to read a PDF on a screen so I forked out for the hardback paper copy. I have to say this is well worth it, incredible scope of coverage and the colouring makes it more easy to understand (none of this stuff is actually 'easy'). This IMO is genuinely THE bible for Machine Learning.

You need to have very great mathematical basis to understand many content in this book, It's a very good one if you want a deeper insight of reinforcement learning.

as desribed.

excellent quality, highly recommended

Arrived in excellent condition.

Very good

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.

Great book! Yet you wont learn much purely by reading it if you are newbie / student in statistics and or data science! Authors are the most revered alive researchers in the field of statistics!