Tuesday 2 March 2010

Machine Learning Textbook

Machine Learning



Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)



Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Get and download textbook Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) for free
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code fo Machine Learning new edition

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Taylor & Francis Ltd | 2009 | 406 pages | ISBN-13: 9781420067187 | ISBN-10: 1420067184 | You save 10%

This is the second volume of a large two-volume editorial project we wish to dedicate to the memory of the late Professor Ryszard S. Michalski who passed away in 2007. He was one of the fathers of machine learning, an exciting and relevant, both from the practical and theoretical points of view, area in modern computer science and information technology. His research career started in the mid-1960s in Poland, in the Institute of Automation, Polish Academy of Sciences in Warsaw, Poland. He left for the USA in 1970, and since then had worked there at various universities, notably, at the Univers

Machine Learning: ECML'97: 9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings: Gerhard Widmer, Maarten Van Someren

Categories: Machine learning. Contributors: Ethem Alpaydin - Author. Format: Hardcover



Machine Learning Textbook



The book is written in an informal, accessible style, complete with pseudo-code fo

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