Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning 下載 mobi epub pdf 電子書 2025

Christopher Bishop
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Springer 2007-10-1 Hardcover 9780387310732

具體描述

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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##斷斷續續看到現在大概完成瞭前11章,其間收集瞭一些資料,書評等完整看過之後再補上。 PRML的數學不是很大問題,因為很多用到的技巧都給齣瞭(大量齣現在第2章,少量齣現在第8章),或者是以附注的形式添加到瞭習題中,而習題是有答案的。 主要障礙是書中的錯誤很多,有英文版錯...  

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##: TP391.4/B622

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##這兩天因為讀文章的需要,重新翻瞭翻這本書。覺得@raullew在http://book.douban.com/review/4474434/ 中提到的問題的確是這本書的一個缺陷。 是否真正瞭解一個東西,不僅取決於你是否瞭解這個東西的特性,還取決於你能不能把它和相似的東西區分開。比如說,你要學習什麼是貓,...  

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##準確說是讀過一兩章... 兩年多以前有個Machine Learning課以PRML為參考書,當時就覺得這書相當的好。可惜一直以來沒認真讀完。最近稍閑終於重新讀瞭一遍,比較有收獲。 這書給人的最大的印象可能是everything has a Bayesian version或者說everything can be Bayesianized,比如PRML至少給齣瞭以下Bay...  

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