The Elements of Statistical Learning

By Trevor Hastie, Robert Tibshirani, Jerome Friedman

Book cover of The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Book description

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour graphics. It is a…

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Why read it?

2 authors picked The Elements of Statistical Learning as one of their favorite books. Why do they recommend it?

This book might as well be called Introduction to machine learning, and it is probably one of the only books truly deserving of the title. Did you know neural networks have been used for decades to scan checks at the bank? They are called Boltzman Machine. Have you ever heard of how decision trees were used in old-school data mining? You could only get them from proprietary software packages from the early 2000s.

In quant trading, you will constantly face compute power constraints, so it is invaluable to understand the mathematical foundations of the most old-school machine learning methods…

From Chris' list on mathematics for quant finance.

I’ve written 31 books on statistics, machine learning, AI, and related areas. But I wish I’d written this one. It’s a superb outline of modern statistical learning theory, encompassing cutting-edge statistical and machine learning methods. I have found it immensely valuable as a source of clear descriptions of the range of modern tools, including methods such as neural networks, ensemble methods, support vector machines, and putting them into context. Liberally illustrated with examples, it enables the reader to see how and why the methods work, and what sort of questions can be answered by the different methods. 

From David's list on statistics from a statistician.

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