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
发表于2024-11-22
Machine Learning pdf epub mobi txt 電子書 下載 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.
不夠係統,有點亂,小錯有點多。瑕不掩瑜,仍是經典。Machine Learning就兩本書,PRML和這本。
評分剛剛翻自己mark過的讀過的書,發現18-19年的讀書痕跡有點淡。大概因為很多時間花在讀課本讀雜誌上麵瞭。
評分剛剛翻自己mark過的讀過的書,發現18-19年的讀書痕跡有點淡。大概因為很多時間花在讀課本讀雜誌上麵瞭。
評分內容很全麵,但感覺章節安排的順序可以稍微調整一下。
評分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
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...
評分哥们就是一个苦逼的本科小民工啊,在ml上完全没有受到过系统的学习,从大约1年半前开始接触机器学习至今,总共看过AG的video,看过《机器学习》和《模式分类》,后来又看了李航的《统计学习方法》,啃过《prml》,学到的东西总感觉零零散散,由于远离ml的圈子,缺乏对这个领域...
評分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。 ==========================================================================================================================================================
評分我们正准备读这本书,Machine Learning A Probabilistic Perspective 读书会请加qq群177217565,也讨论Pattern Recognition And Machine Learning。
Machine Learning pdf epub mobi txt 電子書 下載 2024