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Two of the authors co-wrote The Elements of Statistical Learning Hastie, Tibshirani and Friedman, 2nd 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 algebra. Gareth James is a professor of data sciences and operations 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 grew out of his MBA elective courses in this area. Daniela Witten is an associate professor of statistics and biostatistics at the University of Washington. Her research focuses largely on statistical machine learning in the high-dimensional setting, with an emphasis on unsupervised learning. Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning.

Wake me up when we get to Support Vector Machines! Noah Mackey. About this Book. R Code for Labs. Data Sets and Figures. ISLR Package. Get the Book. Star 3. Branches Tags. Could not load branches. Could not load tags. Latest commit. JWarmenhoven Add files via upload. Add files via upload.

Git stats commits. Failed to load latest commit information. Just want to predict sales, not to know which media is more important. For example 1 Which predictors actually affect the response? How much impact does TV budges have on the sales. Which media generate the biggest boost in sales? A two-step model based approach: 1 Come up with a model some functional form assumption about f.

The most common example is a linear model. I Although it is almost never correct, a linear model often serves as a good and interpretable approximation to the unknown true f X.

I The most common approach is ordinary least squares OLS.