• Title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
• Author(s) Trevor Hastie, Robert Tibshirani, Jerome Friedman
• Publisher: Springer; 2nd edition (2016); eBook (Online Corrected 12th printing - Jan 13, 2017)
• Hardcover: 745 pages
• eBook: PDF (764 pages)
• Language: English
• ISBN-10: 0387848576
• ISBN-13: 978-0387848570
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Book Description

This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting - the first comprehensive treatment of this topic in any book.

• Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area.

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• Amazon (An Introduction to Statistical Learning)

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• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
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