理解机器学习
Understanding Machine Learning
Shai Shalev-Shwartz、Shai Ben-David · 初版 2014 年
12人推荐
16位读者提及
18次提及
为什么值得读
- 专注于理论推导而非库调用的短期速成
- 涵盖机器学习所需的完整数学基础知识
- 结合算法伪代码帮助将理论桥接到实现
“机器学习是计算机科学中发展最快的领域之一，具有广泛的应用前景。本教材旨在以系统化、讲求原则的方式介绍机器学习及其所提供的算法范式。本书对机器学习底层的基本思想进行了广泛的理论阐述，并推导了将这些原理转化为实用算法的数学过程。在介绍该领域基础知识之后，本书涵盖了以往教材未曾涉及的广泛核心主题。其中包括对……的讨论
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我非常喜欢 Ben-David（和 Shalev-Schwartz）写的《Understanding Machine Learning》。不过这本书本质上是讲理论的，可不是那种“10天精通 torchsorflow 机器学习”。
I really enjoyed Ben-David (and Shalev-Schwartz)'s book "Understanding machine learning". It's essentially about theory though, not "learn all about machine learning in torchsorflow in 10 days".
我粗略翻过 Mohri 的那本书，感觉它涵盖的知识点有点窄。如果想打好 ML 的数学底子，我推荐 Shai Shalev-Shwartz 写的《Understanding Machine Learning: From Theory to Algorithms》。作者网站上提供了简写版的免费下载：http://www.cs.huji.ac.il/~shais/Handouts.pdf
I skimmed over Mohri's book and I think the topics it covers are quite narrow. For mathematical foundations of ML, I would recommend the book "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz. A brief version of the book is available to download on the author's website: http://www.cs.huji.ac.il/~shais/Handouts.pdf
教科书我推荐《Understanding Machine Learning》，我之前上过一门用它当教材的课，体验相当不错。http://www.cambridge.org/al/academic/subjects/computer-scien...
I recommend Understanding Machine Learning for a textbook, as I rather enjoyed a class using it. http://www.cambridge.org/al/academic/subjects/computer-scien...
……理论、凸优化算法。Nesterov - 讲义 (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.693...) 统计/理论方向：Shai Shalev-Schwartz 和 Shai Ben-David 的《Understanding Machine Learning》(http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...)，Mohri 的《Foundations of Machine Learning》(https://cs.nyu.edu/~mohri/mlbook/)。上面两门课可以结合 SSS 的在线学习……
…Theory, Convex Optimization Algorithms. Nesterov - Lecture Notes (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.693...) Statistical/Theoretical: Shai Shalev-Schwartz & Shai Ben-David's Understanding Machine Learning (http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...) Mohri's Foundations of Machine Learning (https://cs.nyu.edu/~mohri/mlbook/) The two above courses could share SSS's Online Learning…
……我通常会推荐 Jeff Patton 的《User Story Mapping》和 Scott Berkun 的《Making Things Happen》，两本都是极好的实操指南，有丰富的案例可以直接借鉴。如果你数学不错，我推荐 Shalev-Shwartz 和 Ben-David 写的《Understanding Machine Learning from Theory to Algorithm》，它涵盖了 ML 所需的全部数学知识，而且还提供了一些用于实现的伪代码，有助于架起通往实际软件开发的桥梁……
…typically recommend User Story Mapping by Jeff Patton and Making Things Happen by Scott Berkun, which are both excellent how-tos with great examples to build on. If you're strong in math, I recommend Understanding Machine Learning from Theory to Algorithm by Shalev-Shwartz and Ben-David, as it has all the mathematics for ML and actually has some pseudocode for implementation which helps bridge the gap into actual software development…
……一组（大多是离散的）随机变量并从中进行推断。所以这讲的是边缘化、条件化以及压缩的高效聪明的办法。Shai Shalev-Shwartz 和 Shai Ben-David 的《Understanding Machine Learning From Theory to Algorithms》，2014年版。这也是一本神书。你会在里面看到大量的 KTC，不过大多是在凸性和对偶性的框架下展开的。
…a (mostly discrete) set of random variables and draw inferences from them. So this is about smart and efficient ways to marginalize and condition and compress. Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning From Theory to Algorithms, 2014 This is also a great book. You will see a lot of KTC here but mostly within the framework of convexity and duality.