eBook An Introduction to Statistical Learning: With Applications in R – Albawater.co

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, andColor 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 platformTwo of the authors co wrote The Elements of Statistical Learning Hastie, Tibshirani and Friedman, nd edition, 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 algebraGareth James is a professor of statistics at 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 grew out of his MBA elective courses in this area Daniela Witten is an assistant professor of biostatistics at University of Washington Her research focuses largely on high dimensional statistical machine learning She has contributed to the translation of statistical learning techniques to the field of genomics, through collaborations and as a member of the Institute of Medicine committee that led to the report Evolution of Translational OmicsTrevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co authors of the successful textbook Elements of Statistical Learning Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title Hastie co developed much of the statistical modeling software and environment in R S PLUS and invented principal curves and surfaces Tibshirani proposed the lasso and is co author of the very successful An Introduction to the Bootstrap