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… this is a welcome update to an already fine book, which will surely reinforce its status as a reference.” (Gilles Blanchard, Mathematical Reviews, Issue 2012 d), “The book would be ideal for statistics graduate students … . Math books, at least data science texts, can usually be divided into those which are easy to read but contain little technical rigor and those which are written with a scientific approach to methodology but are so equation dense that it’s hard to imagine them being read outside an advanced academic setting. This shopping feature will continue to load items when the Enter key is pressed. Many of these tools have common underpinnings but are often expressed with different terminology. Use the Amazon App to scan ISBNs and compare prices. Reviewed in the United Kingdom on September 18, 2019. Something went wrong. This IMO is genuinely THE bible for Machine Learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. A lot of typos in the Kindle book version of the text, Reviewed in the United Kingdom on May 23, 2020. Something we hope you'll especially enjoy: FBA items qualify for FREE Shipping and . Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. My background in statistics, statistical mechanics, and stochastic theory is old, but I'm not a zero at it. Access codes and supplements are not guaranteed with rentals. Arrogant but essential; didactic incoherence; an unfriendly book! The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) 2nd Edition, Kindle Edition by Trevor Hastie (Author), Robert Tibshirani (Author) June 20, 2015. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. There was an error retrieving your Wish Lists. Fulfillment by Amazon (FBA) is a service we offer sellers that lets them store their products in Amazon's fulfillment centers, and we directly pack, ship, and provide customer service for these products. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. A pedagogical disaster. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. Our machine learning course has two recommended literatures of which "The Elements of Statistical Learning" (ESL) was one of them, while the primary was Pattern Recognition and Machine Learning (PRML). The Elements of Statistical Learning (2nd edition) During the past decade has been an explosion in computation and information technology. 3), "This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters … were included. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. The book is very well written, with informative graphics on almost every other page. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. Reviewed in the United States on October 6, 2018. … These additions make this book worthwhile to obtain … . Reviewed in the United States on February 26, 2016. Many examples are given, with a liberal use of color graphics. (Book Review Editor, Technometrics, August 2009, VOL. The book is very well written, with informative graphics on almost every other page. Reviewed in the United States on February 4, 2018. The book was delivered quickly and seemed in perfect shape except for one very crucial flaw. Read 47 reviews from the world's largest community for readers. Some of the derivations are things you would see on the blackboard of an advanced course in statistics, not machine learning, and take careful notes of. Reviewed in the United States on October 6, 2018. pdfs / The Elements of Statistical Learning - Data Mining, Inference and Prediction - 2nd Edition (ESLII_print4).pdf Go to file Go to file T; Go to line L; Copy path tpn Fix permissions. During the past decade there has been an explosion in computation and information technology. Is it a good investment, statistically speaking!" It is a valuable resource for statisticians and anyone interested in data mining in science or industry. Does this book contain inappropriate content? "Like the first edition, the current one is a welcome edition to researchers and academicians equally…. 51, NO. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. I would return the kindle book but the time limit for that is exceeded in Amazon! So I complain in this review instead. … this is a welcome update to an already fine book, which will surely reinforce its status as a reference.” (Gilles Blanchard, Mathematical Reviews, Issue 2012 d), “The book would be ideal for statistics graduate students … . Reviewed in the United Kingdom on July 29, 2014. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. To get the free app, enter your mobile phone number. I ordered the book for delivery in advance of a trip and only now got to see it. I emailed one one of the authors for suggestions of a companion text, but received no reply. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). It looks great and inviting. In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning.

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