Abstract

This book provides an introduction to statistical learning methods and is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.

Keywords

Computer scienceStatistical learningField (mathematics)Artificial intelligenceMachine learningCluster analysisStatistical modelData scienceMathematics

Affiliated Institutions

Related Publications

Markov Chains

James R. Norris

Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculat...

1997 Cambridge University Press eBooks 1112 citations

Matrix Analysis

Roger A. Horn , Charles R. Johnson

In this book the authors present classical and recent results for matrix analysis that have proved to be important to applied mathematics. Facts about matrices, beyond those fou...

1985 Cambridge University Press eBooks 9982 citations

Publication Info

Year
2018
Type
book
Citations
2988
Access
Closed

External Links

View on DOI.org

Social Impact

Altmetric
PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

2988
OpenAlex

Cite This

Gareth James, Daniela Witten, Trevor Hastie et al. (2018). An Introduction to Statistical Learning: with Applications in R. . https://doi.org/10.25334/q4ht55

Identifiers

DOI
10.25334/q4ht55