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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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...  

评分

评分

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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我是学工程的,读过很多统计,模式识别,数据挖掘的书。比如Andrew Gelman 的 Beyesian data Analysis; Trevor Hastie 的 The Elements of Statistical Learning等等。。。。 我发现一个问题,但凡是统计系人出的书,我读起来都特别困难,比如以上提到的两本,基本读到第四第...  

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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...  

用户评价

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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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快看完了,近期不准备再读了。就是纯粹从bayesian角度开讲机器学习,确实是很有深度的一本书。不过The Element of Statistical Learning出第二版了,我觉得最好还是那本吧

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这本书断断续续看了好几年,腆着脸标个“已读”吧

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比Murphy那本好读的多

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快看完了,近期不准备再读了。就是纯粹从bayesian角度开讲机器学习,确实是很有深度的一本书。不过The Element of Statistical Learning出第二版了,我觉得最好还是那本吧

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