Reviewed in the United States on June 4, 2017. Robert Tibshirani. 2009, Corr. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. Reviewed in the United Kingdom on December 12, 2018. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. Professor Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics, and machine learning. The pdf of SLS will be available for download December 1, 2015, with permission from the publisher. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. Trevor Hastie Prior to joining Stanford University, Professor Hastie worked at AT&T Bell Laboratories, where he helped develop the statistical modeling environment popular in the R computing system. Anyone who wants to intelligently analyze complex data should own this book." Heavier books on maths and stats with 500+ pages are not for me, as I generally get lost and find hard to follow those books. 2013, Corr. Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. TREVOR HASTIE. He has published over 80 papers and one book in these areas, received the COPSS Presidents’ Award in 2014, and was a section lecturer at the International Congress of Mathematicians in 2014. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. Gareth James is a professor of data sciences and operations at the University of Southern California. Professor Tibshirani was a recipient of the prestigious COPSS Presidents’ Award in 1996 and was elected to the National Academy of Sciences in 2012. Trevor John Hastie (born 27 June 1953) is a South African and American statistician and computer scientist. Professor Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics, and machine learning. I have a joint appointment in the Department of Statistics at Stanford University, and the Division of Biostatistics of the Health, Research and Policy Department in the Stanford School of Medicine. Jerome Friedman . It also analyzes reviews to verify trustworthiness. I believe it's a bit misleading saying an "Introduction" when certain knowledge appears to be assumed by the authors. © Statistical Learning with Sparsity 2015. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Do an Internet search for the authors online videos to see if you will understand what they are saying. You need a bit of maths/stats knowledge beforehand, Reviewed in the United Kingdom on March 10, 2020. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. Everyday low prices and free delivery on eligible orders. Second Edition February 2009. Springer; 1st ed. 9th printing 2017 by Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome (ISBN: 9780387848570) from Amazon's Book Store. Top subscription boxes – right to your door, © 1996-2020, Amazon.com, Inc. or its affiliates. If you either have some statistics background or programming experience, self-study is also an option. In 2009, Stanford Statistics professors Hastie/Tibshirani/Friedman wrote 'The Elements of Statistical Learning', a book that demands a Master's or Doctoral level knowledge of Mathematical Statistics. Your recently viewed items and featured recommendations, Select the department you want to search in, An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics), 1st ed. During the past decade there has been an explosion in computation and information technology. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Find all the books, read about the author, and more. Even if you don’t want to become a data analyst―which happens to be one of the fastest-growing jobs out there, just so you know―these books are invaluable guides to help explain what’s going on.” (Pocket, February 23, 2018). What's new in the 2nd edition? Statistical Learning: Data Mining, Inference, and Prediction. . If you are not a mathematician, and you just need to apply data analytics to your research or in your job, this book will really help you. Start anytime in self-paced mode. Robert Tibshirani, Professor in the Departments Health Research and Policy and Statistics… Reviewed in the United Kingdom on March 6, 2018. Honestly, this is the best statistics text I've ever read. 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. If you use any of these figures in a presentation or lecture, somewhere in your set of slides please add the paragraph: "Some of the figures in this presentation are taken from "An Introduction to Statistical Learning, with applications in R" (Springer, 2013) with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani " They say that it is more thorough, but for what I need to do in my research this book is already enough. 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. He also co-authored the first study that linked cell phone usage with car accidents, a widely cited article that has played a role in the introduction of legislation that restricts the use of phones while driving. After viewing product detail pages, look here to find an easy way to navigate back to pages you are interested in. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.