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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##断断续续看到现在大概完成了前11章,其间收集了一些资料,书评等完整看过之后再补上。 PRML的数学不是很大问题,因为很多用到的技巧都给出了(大量出现在第2章,少量出现在第8章),或者是以附注的形式添加到了习题中,而习题是有答案的。 主要障碍是书中的错误很多,有英文版错...  

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##我们已经读完了Pattern Recognition And Machine Learning ,写的非常优美的一本书,另外我们正准备读MLAPP,欢迎加群177217565讨论。请在群申请理由里用简短的话描述一个算法的关键思想。  

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##终于熬过了这门课。Bishop真是太牛逼了!

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##只读了前几章...

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在研一的下学期的时候,看了前三章。写得非常好,看着就不想放下。后来由于有其他事,就先停了下来。现在经过一年的实习,对机器学习感觉也算入门了,准备着手再开始看,相信这次会有完全不同的感觉。大家一起加油,PRML真是经典!  

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##觉得可能不如ESL,但是这书贵在太多人一起看了各种笔记丰富,给马春鹏这位小弟弟跪了!

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