图书标签: 机器学习 MachineLearning 数据挖掘 计算机 计算机科学 概率 统计 人工智能
发表于2024-11-22
Machine Learning pdf epub mobi txt 电子书 下载 2024
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.
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。
评分内容较新及全面。
评分看的时候不会写代码。可视化做的异常好。
评分翻了两章果断放下。不懂的人看不懂,懂的人看你干嘛。‘全’是逼格最低的优点。
评分这本书优点就是很全面,千余页的大部头,啥都有。缺点也是很全面,每一个点都不太细致,还需要自己去找论文看。
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...
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...
评分-----------------------------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...
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Machine Learning pdf epub mobi txt 电子书 下载 2024