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Created/Published/Taught by:

Springer

Trevor Hastie

Robert Tibshirani

Jerome Friedman

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Tags: boosting / classification trees / ensemble learning / high-dimensional problems / kernel methods / linear discriminant analysis / linear models / logistic regression / model selection / neural networks / regression / regularization / statistics / supervised learning / Support Vector Machines (SVMs) / unsupervised learning

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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. 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.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for `wide” data (p bigger than n), including multiple testing and false discovery rates. `

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful {italics An Introduct ion to the Bootstrap}. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

Free PDF Version: http://statweb.stanford.edu/~tibs/ElemStatLearn/

Go to Content: The Elements of Statistical Learning

## By trianglegirl May 2, 2017 - 4:04 am

This is a fabulous reference book which I use frequently. The book covers a large range of topics in a well organised manner and goes deep enough into the mathematics whilst also providing plenty of intuition.

It’s not a book that I would read cover to cover, but something to delve in and out of frequently. It’s the first place I look when I’m working in a new areas.

Highly recommended.

Intermediate- comfortable with data science / has relevant prerequisites / familiar with this topicAdvanced Intermediate - had prior experience applying this topic

(4) A Lot

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Some but not most

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Confidence with mathematics, algebra manipulation etc.

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It's free - but the quality is very high.

I use this book as a reference book. If I have time I will read a chapter for fun.

I have "owned" this book for 5 years and refer to it every couple of months

Learner interested in getting a strong statistical grounding.