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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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...  

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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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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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在Bishop的这本PRML之前,学习machine learning的标准教材一般是Tom Mitchell的machine learning以及Duda&Hart的Pattern Classification (那个年代ML与PR非常大的重合之处)。不可否认,这两本书都是ML领域的经典教材,但是由于成书时间太早,基本上都属于上古读物,已经不大适...  

用户评价

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机器学习的好教材,较深入

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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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容易理解

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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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估计很长时间内不会再翻了

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