Pattern Recognition and Machine Learning

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Christopher Bishop
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Springer 2007-10-1 Hardcover 9780387310732

具体描述

Christopher M. Bishop is Deputy Director of Microsoft Research Cambridge, and holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. His previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. 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.

The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

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what can i say. It is simply THE book for ml. 真本书的推导已经很清楚了,除了线性代数和简单微积分也没啥别的数学了。如果真的看起来觉得难得话,真的不适合做这个领域了。

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##这两天因为读文章的需要,重新翻了翻这本书。觉得@raullew在http://book.douban.com/review/4474434/ 中提到的问题的确是这本书的一个缺陷。 是否真正了解一个东西,不仅取决于你是否了解这个东西的特性,还取决于你能不能把它和相似的东西区分开。比如说,你要学习什么是猫,...  

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what can i say. It is simply THE book for ml. 真本书的推导已经很清楚了,除了线性代数和简单微积分也没啥别的数学了。如果真的看起来觉得难得话,真的不适合做这个领域了。

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