There is a person named Gareth James. He is a professor at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book was created by his courses in this area.

The person is Daniela Witten. At the University of Washington, he is an associate professor of statistics and biostatistics. Her research focuses on statistical machine learning in the high-dimensional setting.

There is a person named Trevor Hastie. And. Robert is the son of Robert. The professors are co-authors of the textbook Elements of Statistical Learning. The popular book of that title was written by Hastie and the others. The principal curves and surfaces were invented by Hastie. An introduction to the bootstrap was co-authored by the authors of the lasso.

–This text refers to the hardcover edition.

There is an introduction to statistical learning. 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 is provided. Some of the most important modeling and prediction techniques are presented in this book. Linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more are topics. Real-world examples and color graphics are used to show 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.

The Elements of Statistical Learning is a popular reference book for statistics and machine learning researchers. There is an introduction to statistical learning. Many of the same topics are accessible to a much broader audience. The book was written for statisticians and non-statisticians who wish to use cutting-edge statistical learning techniques to analyze their data. The text only takes a previous course in linear regression.

–This text refers to the hardcover edition.