- #An introduction to statistical learning review how to#
- #An introduction to statistical learning review software#
- #An introduction to statistical learning review code#
The end-of-chapter exercises make the book an ideal text for both classroom learning and self-study. “Written by four experts of the field, this book offers an excellent entry to statistical learning to a broad audience, including those without strong background in mathematics.
#An introduction to statistical learning review how to#
ISL contains 12 very valuable R labs that show how to use many of the statistical learning methods with the R package ISLR. the homework problems in ISL are at a Master’s level for students who want to learn how to use statistical learning methods to analyze data.
“This book (ISL) is a great Master’s level introduction to statistical learning: statistics for complex datasets. I am glad that this was written.” (Mary Anne, Cats and Dogs with Data,, June, 2014)
#An introduction to statistical learning review code#
I am having a lot of fun playing with the code that goes with book. it adds information by including more detail and R code to some of the topics in Elements of Statistical Learning. “The stated purpose of this book is to facilitate the transition of statistical learning to mainstream. I will surely use many examples, labs and datasets from this book in my own lectures.” (Pierre Alquier, Mathematical Reviews, July, 2014) the book will certainly be useful to many people (including me). The code for all the statistical methods introduced in the book is carefully explained. “The book provides a good introduction to R. Poullis, Computing Reviews, September, 2014) Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.
#An introduction to statistical learning review software#
Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Her research focuses largely on statistical machine learning in the high-dimensional setting, with an emphasis on unsupervised learning. The conceptual framework for this book grew out of his MBA elective courses in this area.ĭaniela Witten is an associate professor of statistics and biostatistics at the University of Washington. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. Gareth James is a professor of data sciences and operations at the University of Southern California. "synopsis" may belong to another edition of this title. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. 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.
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. Color graphics and real-world examples are used to illustrate the methods presented. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. This book presents some of the most important modeling and prediction techniques, along with relevant applications. 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.