Foundations of Machine Learning

Foundations of Machine Learning (Adaptive Computation and Machine Learning Series)
by Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar
Print Length 页数: 480 pages
Publisher finelybook 出版社: MIT Press (7 Sept. 2012)
Language 语言: English
ISBN-10: 026201825X
ISBN-13: 9780262018258
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.
本研究生课本介绍机器学习中的基本概念和方法。它描述了几种重要的现代算法，提供了这些算法的理论基础，并说明了其应用的关键方面。作者的目的是提出新的理论工具和概念，同时给出相对较高级主题的简明证明。机器学习的基础填补了一个通用教科书的需要，该教科书还提供了理论细节和重点证明。在这里更详细地讨论了经常受到关注不足的某些话题;例如，整个章节专门用于回归，多类分类和排名。前三章为后续的理论基础奠定了基础，但其余的章节大多是自给自足的。附录提供了一个简洁的概率评估，一个简明的凸优化，集中范围的工具，以及本书中使用的矩阵和规范的几个基本属性。本书面向机器学习，统计学和相关领域的研究生和研究人员;它可以用作研究研讨会的教科书或参考文本。

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