Pattern Recognition and Machine Learning

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Christopher Bishop
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Springer 2007-10-1 Hardcover 9780387310732

具体描述

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.

用户评价

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##机器学习的好教材,较深入

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##结构清晰,内容齐全,是初学者不可多得的好书。

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##结构清晰,内容齐全,是初学者不可多得的好书。

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##结构清晰,内容齐全,是初学者不可多得的好书。

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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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##这本书最近开源了: [https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/] 作为上课的教材读的,内容结构上比较全面。从基本的问题出发,对于每一个问题和范式的来由解释得比较详细清楚,也因而显得小章节间的逻辑关系 (有时) 堆得比...  

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