A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.
This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (inc...
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.
This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.
Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
作者简介
· · · · · ·
Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision, and natural language understanding.
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34册)，
这套丛书还有
《Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)》《Introduction to Machine Learning》《Semi-Supervised Learning》《Causation, Prediction and Search》《Introduction to Natural Language Processing》
等
。
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/神经科学底层民工，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...
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Kai_S
MIT Press2012版
pUck
MIT Press2012版
小夫
MIT Press2012版
飞天遁地小花侠
MIT Press2012版
aeitus
1 有用 Bodhin 2023-03-24 19:47:46 北京
涉及面很广，非常适合本科生教学。第一部分是概率、矩阵和优化之类的基础知识。后面才会介绍各个模型。很多公式我都跟着推导过，公式推导比较详细，很少出现不知道怎么就跳到这一步的情况。既有广度又有深度，也可以放在手边做工具书，遇到想不起来的查一下。
2 有用 Roy 2023-07-13 09:11:23 江苏
第二版加了好多好多内容 基础+Advanced Topics两本加起来有2400+页 前沿章节的方法和文献都是出版时最新的内容 完全是一本非常好的机器学习百科全书
1 有用 massquantity 2023-05-07 10:31:04 上海
在现在(2023年) 这个时间节点，如果是在理论角度系统学习机器学习，这本可能是最合适的。看来有了第一版的经验作者写起来更加顺畅了。
6 有用 zhaoyuan 2021-12-11 14:14:14
用统一的概率视角综述了机器学习的各个领域，知识点和approach都很现代
0 有用 易山 2023-08-11 22:24:16 江苏
技术很新，sota也有标注；传统模型的公式推倒很详细
0 有用 易山 2023-08-11 22:24:16 江苏
技术很新，sota也有标注；传统模型的公式推倒很详细
2 有用 Roy 2023-07-13 09:11:23 江苏
第二版加了好多好多内容 基础+Advanced Topics两本加起来有2400+页 前沿章节的方法和文献都是出版时最新的内容 完全是一本非常好的机器学习百科全书
1 有用 massquantity 2023-05-07 10:31:04 上海
在现在(2023年) 这个时间节点，如果是在理论角度系统学习机器学习，这本可能是最合适的。看来有了第一版的经验作者写起来更加顺畅了。
1 有用 Bodhin 2023-03-24 19:47:46 北京
涉及面很广，非常适合本科生教学。第一部分是概率、矩阵和优化之类的基础知识。后面才会介绍各个模型。很多公式我都跟着推导过，公式推导比较详细，很少出现不知道怎么就跳到这一步的情况。既有广度又有深度，也可以放在手边做工具书，遇到想不起来的查一下。
6 有用 zhaoyuan 2021-12-11 14:14:14
用统一的概率视角综述了机器学习的各个领域，知识点和approach都很现代