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 a...
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.
To understand these terms, you first need to understand the concept of likelihood. Assume you have a probability distribution - or rather family of such distributions - p(x;w) which assigns a probability to each data point x, given a specific setting of its parameters w. That is, different values of the parameters, w, will change the probability assigned to each data point, x.
Now, since different parameters correspond to different distributions, we can tune the parameters in such a way that the data that we observe, D, is assigned a high probability and possible data that we don't observe is assigned a low probability. To this end we define the likelihood function L(D;w) = product_{x in D} p(x;w). That is the likelihood is just the joint probability of the observed data as a function of ... (查看原文)
In particular, we define machine learning as a set of methods that can
automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (查看原文)
Adaptive Computation and Machine Learning(共36册)，
这套丛书还有
《Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning)》《Introduction to Machine Learning》《Learning Kernel Classifiers》《Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)》《Machine Learning from Weak Supervision》
等
。
One of the textbook used in the course "Introduction to Machine Learning" at CMU in the forth year of PhD. Good for reference, but not good for reading or learning. Most ML books are not worth reading...One of the textbook used in the course "Introduction to Machine Learning" at CMU in the forth year of PhD. Good for reference, but not good for reading or learning. Most ML books are not worth reading if you are a math student, including this one.(展开)
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...
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作为一名心理学底层民工，关于machine learning/deep learning的知识纯粹是翻书自学的。中间大约断断续续翻过李航的统计学习方法， 频率派的ISL ，The Elements of Statistical Learning，公认的贝叶斯教材PRML以及Gelman所写的BDA3的部分章节。与前面这些经典教材相比较起来，...
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Kai_S
飞天遁地小花侠
aeitus
The MIT Press2022版
0 有用 Bis 2021-10-17 06:38:29
.... 从推荐里拿出去
1 有用 Ginger 2022-09-05 21:28:58 加拿大
有点百科全书式。非常庞大庞杂。语言不是很友好。但是深度很🉑。比较适合搞PhD的尤其是要做理论的读。
0 有用 施威林先生 2022-01-03 11:34:38
百科全书一般，但是有点太细致了，适合遇到问题时查一查。（补标：2014-2015）
1 有用 ize 2015-06-15 09:58:27
太执着于一个学派也不好。大坑慎入。 Important chapters 4 me: Chaps.3-12, 14, 17, 19 & 25.
0 有用 josuya 2013-01-18 10:01:09
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。
0 有用 Starry 2024-09-29 15:10:41 美国
经典，不知不觉作者另一新书都出了快2年了
0 有用 Sisyphus 2023-12-22 23:19:00 美国
补标
0 有用 Chao Peng 彭超 2023-02-19 07:47:58 北京
和Elements of Statistical Learning 一起看
0 有用 镇守府的尼克桑 2022-12-25 01:01:43 美国
深入Machine Learning内部的数学原理。
2 有用 Rex 2022-12-18 04:50:40 美国
One of the textbook used in the course "Introduction to Machine Learning" at CMU in the forth year of PhD. Good for reference, but not good for reading or learning. Most ML books are not worth reading... One of the textbook used in the course "Introduction to Machine Learning" at CMU in the forth year of PhD. Good for reference, but not good for reading or learning. Most ML books are not worth reading if you are a math student, including this one. (展开)