Pattern Recognition and Machine Learning

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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.

出版者:Springer
作者:Christopher Bishop
出品人:
页数:738
译者:
出版时间:2007-10-1
价格:USD 94.95
装帧:Hardcover
isbn号码:9780387310732
丛书系列:
图书标签:
  • 机器学习 
  • 模式识别 
  • 人工智能 
  • 数据挖掘 
  • 计算机 
  • 计算机科学 
  • MachineLearning 
  • machine 
  •  
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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.

具体描述

读后感

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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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PRML读书会一周年资源汇总:http://weibo.com/p/10080817a99a8dcd9c7e83da56c7ee13ede62a/emceercd?from=page_huati_rcd_more

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两年多以前有个Machine Learning课以PRML为参考书,当时就觉得这书相当的好。可惜一直以来没认真读完。最近稍闲终于重新读了一遍,比较有收获。 这书给人的最大的印象可能是everything has a Bayesian version或者说everything can be Bayesianized,比如PRML至少给出了以下Bay...  

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两年多以前有个Machine Learning课以PRML为参考书,当时就觉得这书相当的好。可惜一直以来没认真读完。最近稍闲终于重新读了一遍,比较有收获。 这书给人的最大的印象可能是everything has a Bayesian version或者说everything can be Bayesianized,比如PRML至少给出了以下Bay...  

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我是一名研一的学生,方向不是机器学习方向,但是对这方面很感兴趣。 看过一篇blog说,当下所说的机器学习其实分两种,一种如本书,可称为统计机器学习,另外一种是人工智能领域,这两种有交叉,但是研究内容有很大不同。 初读这书,刚觉很罗嗦,加上是英语,就觉得有些内容很...  

用户评价

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估计很长时间内不会再翻了

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: TP391.4/B622

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机器学习的好教材,较深入

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上学期上了门课,一点不喜欢!!虽然老师身材不错而且顺了几个八卦。前几天整理房间的时候看到这教材顺路读了下 结果欲罢不能....我果然是犯贱吗?!

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只读了前几章...

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