Machine Learning pdf epub mobi txt 電子書 下載2025
Kevin P. Murphy is Associate Professor in the Department of Computer Science and in the Department of Statistics at the University of British Columbia.
- 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
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.
具體描述
讀後感
判别模型几乎没怎么讲。。 后面各种生成模型，贝叶斯网、随机场、MCMC、HMM。 ==========================================================================================================================================================
評分另外的两本分别是PRML和ESLII。 这本书的成书时间最晚，刚出的时候特意花了90刀从亚马逊买的。 先说说优点：新，全！ 刚说了，相对于另外两本书，由于成书时间较晚，所以涵盖了更多最近几年的hot topic，比如Dirichlet Process，在其他另外两本书中都没有提到过。 更重要的，是...
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...
評分数学本来就弱，啃英文更苦难。。谢谢大家分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分...
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...
用戶評價
Chapter 1-3, 07.09.2019; C4 (Gaussian models) 07.12; C5 (Bayesian statistics) 07.19;C6 (Frequentist statistics) 07.20; C7 (Linear regression) 07.29; C8 (Logistic regression) 08.22
评分:無
评分內容較新及全麵。
评分CSCI 567 Machine Learning 教材。
评分翻瞭兩章果斷放下。不懂的人看不懂，懂的人看你乾嘛。‘全’是逼格最低的優點。
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