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.

用戶評價

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##教材。作者開直升機的。不適閤初學者,david barber即將齣版的新書Bayesian Reasoning and Machine Learning更適閤。

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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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##結構清晰,內容齊全,是初學者不可多得的好書。

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##在瀏覽 scikit-learn 時 無意發現之,貢獻者之一 是清華的Wei Li https://github.com/kuantkid/PRML 發現瞭,分享瞭;未曾使用。 大傢用用看,看看樓下怎麼說:  

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##我是一名研一的學生,方嚮不是機器學習方嚮,但是對這方麵很感興趣。 看過一篇blog說,當下所說的機器學習其實分兩種,一種如本書,可稱為統計機器學習,另外一種是人工智能領域,這兩種有交叉,但是研究內容有很大不同。 初讀這書,剛覺很羅嗦,加上是英語,就覺得有些內容很...  

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##贊揚已經夠多瞭,引用黃亮的話來說下這本書不好的地方。 “這書把machine learning搞得太復雜太瑣碎瞭,而迷失瞭其數學真意。其數學真意應該是簡單統一的幾何意義,而不是滿屏的公式。另外這書理論深度不夠,很多重要但簡單的證明沒講. 簡言之,這書是電子工程師寫的,不是給...  

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