Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for the...
Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.
The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.
For large [;n;], .... the method tends to have a small bias but a large variance, .... smaller values of [;n;] lead to more diverse training samples but their size is significantly less than [;m;], thus the method tends to have a smaller variance but a larger bias. (查看原文)
...The bound suggests seeking a trade-off between reducing the empirical error versus controlling the size of the hypothesis set... This can be viewed as an instance of the so-called [;Occam's\,\,Razor \,\,principle;]...: Plurality should not be posited without neccessity,... (查看原文)
Adaptive Computation and Machine Learning(共36册)，
这套丛书还有
《Machine Learning》《Veridical Data Science》《Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)》《Reinforcement Learning》《Machine Learning for Data Streams》
等
。
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绵羊
机械工业出版社2019版
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机械工业出版社2019版
15 有用 廿五 2014-10-15 10:05:17
入门还行，prerequsite比较少，附录里都包含了；跟Valiant一脉相承，个人觉得可以加个副标题叫“手把手教你推bound”；不适合只对应用感兴趣的读者，算法细节讨论较少，全是分析误差界；笔误略多，看之前一定把作者主页上的勘误表（不全）过一下。
0 有用 少年终将成王 2021-04-12 10:34:24
20考研补卡
0 有用 Tefnut-L 2020-08-01 22:00:59
爆炸...
1 有用 换个ID试试 2017-04-27 15:50:14
Foundations这本书真是给力，手把手教你推bound。如果以后想要懂一些learning theory，这本书是非常好的入门书。当然，只看一遍是肯定不够的，还要多读多感受。唯一缺点是typo太多，影响整体的阅读体验，希望尽快出第二版。
7 有用 谜团 2014-04-10 01:11:14
用PAC框架和VC理论给了BOOST和SVM坚实的基础，书中对regression那一章用PAC和VC来解释为什么需要做L1或者L2的惩罚，给了统一的理论框架，颇有启发性。缺点就是搞计算机的人数学实在是不够严谨。计算机的人的角度和统计的人的角度看同一件事情的不同看法是最有意思的。