Introduction to Statistical LearningAn Introduction toStatistical Learningwith Applications in R,and(corrected 7th printing)Statistical Learning MOOC covering the entire ISL book offered by TrevorHastie and Rob Tibshirani.Start anytime in self-paced mode.This bookprovides an introduction to statistical learning methods. It is aimed for upperlevel undergraduate students, masters students and Ph.D. Students in thenon-mathematical sciences. The book also contains a number of R labs withdetailed explanations on how to implement the various methods in real lifesettings, and should be a valuable resource for a practicing data scientist.Winner of the 2014 Eric Ziegel award from Technometrics.For a more advanced treatment of these topics.Slides and videos for Statistical Learning MOOCby Hastie and Tibshirani available separately. Slides and video tutorials related to this bookby Abass Al Sharif can be downloaded.'
![]() ![]()
An Introduction to Statistical Learning (ISL)' by James, Witten, Hastie andTibshirani is the 'how to' manual for statistical learning. Inspired by 'TheElements of Statistical Learning' (Hastie, Tibshirani and Friedman), this bookprovides clear and intuitive guidance on how to implement cutting edgestatistical and machine learning methods. ISL makes modern methods accessible toa wide audience without requiring a background in Statistics or ComputerScience. The authors give precise, practical explanations of what methods areavailable, and when to use them, including explicit R code. Anyone who wants tointelligently analyze complex data should own this book.
![]()
Larry Wasserman,Professor, Department of Statistics and Department of Machine Learning, CMU.As a textbook for an introduction to data science through machine learning,there is much to like about ISLR. It’s thorough, lively, written at levelappropriate forundergraduates and usable by nonexperts. It’s chock full of interesting examples of how modern predictive machine learning algorithms work (anddon’t work) in a variety of settings.'
Matthew Richey, The American Mathematical Monthly, Vol. 7 (August-September2016).' I just wanted tothank you all for the textbook “An Introduction to Statistical Learning” thatyou have contributed to as authors. As a junior at university, it is by far themost well-written textbook I have ever used, a sentiment mirrored by all myother classmates. One friend, graduating this spring with majors in Math andData Analytics, cried out in anger that no other textbook had ever come close tothe quality of this one. You and your team have turned one of the mosttechnical subjects in my curriculum into an understandable and even enjoyablefield to learn about.
Every concept is explained simply, every equationjustified, and every figure chosen perfectly to clearly illustrate difficultideas. This is the only textbook I have ever truly enjoyed reading, and I justwanted to thank you and all other contributors for your time and efforts in itsproduction.'
Cornell Blake, Junior, Ohio State University.' As a former data scientist, there is no question I getasked more than, “What is the best way to learn statistics?” I always give thesame answer: Read An Introduction to Statistical Learning. Then, if you finishthat and want more, read The Elements of Statistical Learning. These two books,written by statistics professors at Stanford University, the University ofWashington, and the University Southern California, are the most intuitive andrelevant books I’ve found on how to do statistics with modern technology. 'Dan Kopf, Reporter, Quartz. Full review.Linear Regression?I covered that last year.Wake me up when we get to Support Vector Machines!Noah Mackey.
Navsea training manual applied engineering principles manual naval sea systems command navy department rev. 1, acn-1, may 2003.
![]() Comments are closed.
|
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
March 2023
Categories |