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Springer An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Name: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Brand: Springer

40099270

AI & Machine Learning

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

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An Introduction to Statistical Learning: with Applications in R Springer Texts in Statistics

Books Subjects Computing Internet Computer Science AI Machine Learning Books Subjects Computing Internet Software Graphics Business Home Office Books Subjects Science Nature Maths Mathematics Education Higher Education Books Subjects Science Nature Maths Mathematics Probability Statistics Books Subjects Scientific Technical Medical Mathematics Applied Mathematics Statistics Probability

Product Type: ABIS BOOK

Brand: Springer

Product Specifications

Brand: Springer
Model: 40099270
Format: hardcover
ASIN: 1071614177
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 30 July 2021
Listed Since: 17 December 2020

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Springer An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

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