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

Created by: Trevor Hastie, Robert Tibshirani, Jerome Friedman

Produced in 2016

icon
Course 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 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 factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.

icon
Instructor Details

Trevor Hastie, Robert Tibshirani, Jerome Friedman

Trevor Hastie was born in South Africa in 1953. He received his university education from Rhodes University, South Africa (BS), University of Cape Town (MS), and Stanford University (Ph.D Statistics 1984). He is a Professor in Statistics and statistics in Stanford University. In 2013 he was named the John A. Overdeck Professor of Mathematical Sciences, and in 2018 was elected to the National Academy of Sciences.

Read More

icon
Reviews

0.0

0 total reviews

5 star 4 star 3 star 2 star 1 star
% Complete
% Complete
% Complete
% Complete
% Complete
% % % % %