Trevor Hastie

Robert Tibshirani

Gareth James

Daniela Witten

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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. 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. 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.

with Applications in R

An Introduction to Statistical Learning

With Applications in R

Introduction to Statistical Learning

Jonathan Taylor

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, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

with Applications in Python

Dr. Patricia Cardner

So, you want to get better at poker? You are committed to this and you are prepared to spend time on it. You ask advice and what do people tell you? Probably something along the lines of “just keep working on it and you’ll get there.” This is wrong. “Just working on it” will not help you. Randomly analysing hands, watching poker on TV or vaguely looking at equity equations won’t cut it. The only approach that will work is the right sort of practice based on a relatively new area of psychological investigation – the science of expertise. This book identifies precisely what this “right sort of practice” entails. It is based around what has become known as purposeful practice. Purposeful practice is the gold standard for anyone who wishes to take full advantage of the remarkable adaptability of the human brain and is the fast track route to improving your poker skills. This book will give you: * A clear theoretical understanding of the science of purposeful practice * Numerous techniques by which this can be adapted to improvement at poker * Specific exercises designed to create an infallible Plan for Poker Improvement

The Modern Approach to Studying Poker

Purposeful Practice for Poker

Michael Krebber

The artist and professor Michael Krebber recently invited his colleagues Gareth James, John Kelsey and Josef Strau to participate in an exhibition with the title "Ical Krbbr Prdly Prsnts Gart Jas, Jon Klsy, Josf Stra." Minus a few of the necessary letters, the artists' names became a wall painting transformed into concrete poetry. As the exhibition freed itself from the curator's reins, the resulting exhibition catalogue also goes against conventional form and order. For example, a foreword by the editor turns out to be an artist's improvised speech on the topic of "Puberty in Painting" "Now I have written: I can't decide any longer for one of these points of views or non-points of views." Artworks are not presented in groups by artist, but rather by associative links of picture strips, found-object texts, prose, drawings and collages.

Gareth James

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