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.
Kevin P. Murphy is Associate Professor in the Department of Computer Science and in the Department of Statistics at the University of British Columbia.
这是我为本书第四次(我买的是第六次印刷,但是是一样的)印刷写的勘误表:https://github.com/ks838/Murphy-Machine-Learning-A-Probabilistic-Perspective-Errata-and-Notes-4th-printing
评分这是我为本书第四次(我买的是第六次印刷,但是是一样的)印刷写的勘误表:https://github.com/ks838/Murphy-Machine-Learning-A-Probabilistic-Perspective-Errata-and-Notes-4th-printing
评分为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这...
评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...
评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...
内容很全面,但感觉章节安排的顺序可以稍微调整一下。
评分刚刚翻自己mark过的读过的书,发现18-19年的读书痕迹有点淡。大概因为很多时间花在读课本读杂志上面了。
评分刚刚翻自己mark过的读过的书,发现18-19年的读书痕迹有点淡。大概因为很多时间花在读课本读杂志上面了。
评分感觉有点泛泛
评分不够系统,有点乱,小错有点多。瑕不掩瑜,仍是经典。Machine Learning就两本书,PRML和这本。
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