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

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.

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

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

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.

Uno de los mejores libros que he leido sobre la tematica de machinelearning. Muy interesante y ligero de leer. Todo perfectamente explicado

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!

Recebi bem antes do previsto, sou Estatístico e não precisei estudar o livro inteiro para saber que é uma obra de excelência, as explicações são muito boas e as figuras coloridas e auto explicativas. Realmente vale a pena a compra. Antes de ler seria bom visitar novamente os livros de cálculo e álgebra linear, e as técnicas estatísticas como regressão linear e não linear.