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.

具體描述

讀後感

評分☆☆☆☆☆

這本書的作者試圖把機器學習進行全景式地展現，根據我有限的機器學習知識，作者把機器學習該有的都涵蓋瞭。這樣做一個非常大的缺陷就是東西太多，講的不夠深入，許多例子都是非常籠統，沒有做詳細解釋，就給瞭一個圖，隨便說瞭幾句，對於一個初學者，怎麼可能理解的瞭。書中...

評分☆☆☆☆☆

為什麼評分這麼高?誰學過，誰學完瞭？為什麼評分這麼高?誰學過，誰學完瞭？為什麼評分這麼高?誰學過，誰學完瞭？為什麼評分這麼高?誰學過，誰學完瞭？為什麼評分這麼高?誰學過，誰學完瞭？為什麼評分這麼高?誰學過，誰學完瞭？為什麼評分這麼高?誰學過，誰學完瞭？為什麼評分這...

評分☆☆☆☆☆

另外的兩本分彆是PRML和ESLII。這本書的成書時間最晚，剛齣的時候特意花瞭90刀從亞馬遜買的。先說說優點：新，全！剛說瞭，相對於另外兩本書，由於成書時間較晚，所以涵蓋瞭更多最近幾年的hot topic，比如Dirichlet Process，在其他另外兩本書中都沒有提到過。更重要的，是...

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這是我為本書第四次（我買的是第六次印刷，但是是一樣的）印刷寫的勘誤錶：https://github.com/ks838/Murphy-Machine-Learning-A-Probabilistic-Perspective-Errata-and-Notes-4th-printing

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用戶評價

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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

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machine learning教材

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太執著於一個學派也不好。大坑慎入。 Important chapters 4 me: Chaps.3-12, 14, 17, 19 & 25.

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應當會像PRML一樣稱雄Machine Learning榜單至少四五年吧

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四星給覆蓋麵。二刷，2019.12.13，有瞭一個更係統性的認識，但是有一些章節的難度比想象中大。