Pattern Recognition and Machine Learning pdf epub mobi txt 电子书 下载 2025

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Pattern Recognition and Machine Learning

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Christopher Bishop



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发表于2025-05-05

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Springer 2007-10-1 Hardcover 9780387310732

Pattern Recognition and Machine Learning epub 下载 mobi 下载 pdf 下载 txt 电子书 下载 2025

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Pattern Recognition and Machine Learning epub 下载 mobi 下载 pdf 下载 txt 电子书 下载 2025

Pattern Recognition and Machine Learning pdf epub mobi txt 电子书 下载



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.

Pattern Recognition and Machine Learning 电子书 下载 mobi epub pdf txt

Pattern Recognition and Machine Learning pdf epub mobi txt 电子书 下载
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##毫无疑问,PRML实乃入门必读之圣书!!!花了一周时间又把公式推了一遍,欲罢不能。另推:David Barber 2012出的Bayesian Reasoning and Machine Learning,其中的Approximate inference部分比PRML讲的好并详述一些最新进展,讨论了几种bound之间的tightening关系。如果想要了解Advanced一点的topic,还可以看Kevin Murphy新出的那本,囊括了更多近年的hot topic入门简介包括deep learning。btw,Kevin现在已经离开UBC,跑到google做knowledge graph,对下一代搜索引擎的query语义理解很有帮助,B厂内部也刚开始无声无息的做这方面的项目。

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##很入门

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##听完coursera上的机器学习的课后觉得ML不过就是拟合函数,但是由于里面并没有详细介绍算法背后的数学推理和我以为会有的关于“学习”“智能”的理论,就找来这本评价较好的书看了一遍。看完这本书后发现ML真的就是拟合函数,而且用的都是一些百年前的数学技巧和几十年前的算法...  

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##教材。作者开直升机的。不适合初学者,david barber即将出版的新书Bayesian Reasoning and Machine Learning更适合。

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##很入门

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