Machine Learning pdf epub mobi txt 電子書下載2025

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Kevin P. Murphy is Associate Professor in the Department of Computer Science and in the Department of Statistics at the University of British Columbia.

出版者:The MIT Press

作者:Kevin P·Murphy

出品人:

頁數:1096

译者:

出版時間:2012-9-18

價格:USD 90.00

裝幀:Hardcover

isbn號碼:9780262018029

叢書系列:Adaptive Computation and Machine Learning

圖書標籤:

機器學習
MachineLearning
數據挖掘
計算機
計算機科學
概率
統計
人工智能

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Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

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讀後感

評分☆☆☆☆☆

Awesome! 1. 与这本书的缘分竟始于化学系图书馆(没有其它两本，PRML or the Elements，也许因为K Murphy是校友的缘故。。不过C Bishop就在附近的Microsoft啊) 最终在黑五我还是买了这本书，装帧结实漂亮；留白够多，这样可以随意增添喜欢的内容和推导。英Amazon比较厚道，便宜...

評分☆☆☆☆☆

这本书的作者试图把机器学习进行全景式地展现，根据我有限的机器学习知识，作者把机器学习该有的都涵盖了。这样做一个非常大的缺陷就是东西太多，讲的不够深入，许多例子都是非常笼统，没有做详细解释，就给了一个图，随便说了几句，对于一个初学者，怎么可能理解的了。书中...

評分☆☆☆☆☆

纯搬运。来自：https://www.cs.ubc.ca/~murphyk/MLbook/errata.html 提交新的bug fix：https://docs.google.com/forms/d/e/1FAIpQLSdOXvmnvuIQn__t0xPyTErj53L-qo_RerImgKbXV4VfLDI6SQ/viewform?formkey=dEp2U2hRWXVpMU5nd05YcEJKVFNUdmc6MQ - preface: added printing hi...

評分☆☆☆☆☆

-----------------------------读完第三章更新------------------------------ 啪啪啪啪啪啪啪啪啪啪啪，先自扇十个大耳光。这本书还是不错的，很深，我写了个第三章的笔记，欢迎拍砖。http://book.douban.com/annotation/23203104/ 第三章可读性比第二章好得多，但是说实话还...

評分☆☆☆☆☆

断断续续读了本书几章内容，并扫了一眼全书，个人感觉这本书就是一本大杂烩。这本书涉及的内容很广，概率图模型、GLM、Nonparametric Method，甚至最近比较火的Deep Learning也包括了。但是，感觉很多地方讲的不是很细致，每每读到关键地方，都有种嘎然而止的感觉。不过还好...