PDF file of book (12th printing with corrections and table of contents [thanks to Kamy Sheblid], Jan 2017) PDF file of book (12th printing with corrections, Jan 2017)

Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Prerequisite: introductory statistical methods course.

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Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition. The Statistical Learning and Data Science Section of the American Statistical Association has selected Trevor for their award named in honor of Professor Leo Breiman. Breiman's work helped to bridge...

SLDMIII is based on "Elements of Statistical Learning", which is a more advanced book than "An Introduction to Statistical Learning". experience in which you get to interact with us and the other participants. Participants get to ask questions during the lectures and the lunch and refreshment breaks

Sep 23, 2014 · In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR).

Stanford University, one of the world's leading teaching and research institutions, is dedicated to finding solutions to big challenges and to preparing students for leadership in a complex world. Throughout the course there are many interactive elements. These include: simulations,walk-throughs that integrate voice and graphics to explain an example of a procedure or a difficult concept, and, most prominently, interactive activities in which students practice problem solving, with hints and immediate and targeted feedback. PDF file of book (12th printing with corrections and table of contents [thanks to Kamy Sheblid], Jan 2017) PDF file of book (12th printing with corrections, Jan 2017) Throughout the course there are many interactive elements. These include: simulations,walk-throughs that integrate voice and graphics to explain an example of a procedure or a difficult concept, and, most prominently, interactive activities in which students practice problem solving, with hints and immediate and targeted feedback.

The contents of the course is based on the book 'Introduction to Statistical Learning' which is much more accessible than 'Elements of Statistical Learning' (also by Hastie and Tibshirani). The presentations are excellent, and it is good to see the experts themselves presenting (with humor). The contents of the course is based on the book 'Introduction to Statistical Learning' which is much more accessible than 'Elements of Statistical Learning' (also by Hastie and Tibshirani). The presentations are excellent, and it is good to see the experts themselves presenting (with humor). For alternatives to Elements of Statistical Learning, my #1 choice by far are the texts by Theodoridis, namely Machine Learning, and Pattern Recognition. These texts are huge and give a very realistic idea of the background it would take to learn this material. First, I think this is a common problem with any book especially if you are new to the area/field. This book in particular focuses on the coverage of topics in machine learning. ABOUT THIS COURSE. This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive ...

The course was pretty much a companion to their textbook "Introduction to Statistical Learning", but "summarizes" the dense, theoretical textbook in a rather applied manner. I absolutely loved the instructors' lectures and R programming demos. The course exercises were fairly straightforward and help reinforce the concepts covered. Robert Tibshirani's main interests are in applied statistics, biostatistics, and data mining. He is co-author of the books Generalized Additive Models (with T. Hastie), An Introduction to the Bootstrap (with B. Efron), and Elements of Statistical Learning (with T. Hastie and J. Friedman).

The course will follow closely Elements of Statistical Learning by Hastie, Tibshirani and Friedman (2008) (second edition), Springer, NY. The book is available from the bookstore (class section). Other References Statistical Learning with Sparsity by Hastie, Tibshirani and Wainwright 2015 (free pdf) PDF file of book (12th printing with corrections and table of contents [thanks to Kamy Sheblid], Jan 2017) PDF file of book (12th printing with corrections, Jan 2017) The Statistical Learning and Data Science Section of the American Statistical Association has selected Trevor for their award named in honor of Professor Leo Breiman. Breiman's work helped to bridge...