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.

用户评价

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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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##终于熬过了这门课。Bishop真是太牛逼了!

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##个人认为这是机器学习领域必读的一本书,甚至是目前最好的书。但这本书太过于 Bayesian, 作者对任何算法都试图从概率和 Bayesian 的角度来进行解释。这本书不适合作为第一本教材,因为其为了将书中内容串联起来,忽视了这些内容的本来面貌,我印象比较深刻的地方有:第1.2.5节...  

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##教材。作者开直升机的。不适合初学者,david barber即将出版的新书Bayesian Reasoning and Machine Learning更适合。

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##毫无疑问,PRML实乃入门必读之圣书!!!花了一周时间又把公式推了一遍,欲罢不能。另推:David Barber 2012出的Bayesian Reasoning and Machine Learning,其中的Approximate inference部分比PRML讲的好并详述一些最新进展,讨论了几种bound之间的tightening关系。如果想要了解Advanced一点的topic,还可以看Kevin Murphy新出的那本,囊括了更多近年的hot topic入门简介包括deep learning。btw,Kevin现在已经离开UBC,跑到google做knowledge graph,对下一代搜索引擎的query语义理解很有帮助,B厂内部也刚开始无声无息的做这方面的项目。

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##赞扬已经够多了,引用黄亮的话来说下这本书不好的地方。 “这书把machine learning搞得太复杂太琐碎了,而迷失了其数学真意。其数学真意应该是简单统一的几何意义,而不是满屏的公式。另外这书理论深度不够,很多重要但简单的证明没讲. 简言之,这书是电子工程师写的,不是给...  

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