One Jump Ahead: Challenging Human Supremacy in Checkers

Springer Science & Business Media
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Why did I write this book? I'm still not sure. After all, I'm a researcher, which means I think I know how to write technical papers. But writing for a n- technical audience is something I know nothing about. It took a lot of effort before I could force myself to sit down to write the first word. Once I did, however, it was hard not to stop! When I started this project, I didn't know that I had a lot to say and, in some sense, the results show this. The book is much longer than I even imagined it would be. Worse yet is that there is a lot of material that I decided not to include. It's a good thing that the publishers decided to limit how long the book could be! However, after much soul searching, I think I now know the reasons why I wrote this book. First and foremost, this book tells an interesting story. It's about the life of a checkers-playing computer program, Chinook, from its creation in 1989 to its retirement in 1996. In reality the story revolves around two people with different views of the program. As the creator of Chinook, I wanted to push the program to become the best player in the world, in much the same way that a father might encourage his son to excel at sports.
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Springer Science & Business Media
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Published on
Mar 9, 2013
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Best For
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Computers / Computer Science
Computers / Information Technology
Computers / Mathematical & Statistical Software
Computers / Programming / General
Mathematics / Discrete Mathematics
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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.

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