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《understanding machine learning From Theory to Algorithms》Shalev-Shwartz S. Ben-David S.
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Understanding Machine Learning:From Theory to Algorithms2014 by Shai Shalev-Shwartz and Shai Ben-DavidPublished 2014 by Cambridge University Press.This copy is for personal use only.Not for distribution.Do not post.Please link to:://.cs.huji.ac.il/-shais/UnderstandingMachineLearningPlease note:This copy is almost,but not entirely,identical to the printed versionof the book.In particular,page mumbers are not identical (but section mumbers are thesame).Shai Shalev-Shwartz and Shai Ben-DavidUNDERSTANDINGMACHINELEARNINGFROM THEORY TO ALGORITHMSUnderstanding Machine LearningMachine leaming is one of the fastest growing areas of computer science,with far-reaching applications.The aim of this textbook is to introducechine learning,and the algorithmic paradigms it offers,in a princi-pled way.The book provides an extensive theoretical account of thefundamental ideas underlying chine learning and the theticalderivations that transform these principles into practical algorithms.Fol-lowing a presentation of the basics of the field,the book covers a widearray of central topics that have not been addressed by previous text-books.These include a discussion of the computational complexity ofleaming and the concepts of convexity and stability;important algorith-mic paradigms including stochastic grant descent,neural networks,and structured output learning:and emerging theoretical concepts such asthe PAC-Bayes approach and compression-based bounds.Designed foran advanced undergraduate or beginning graduate course,the text kesthe fundamentals and algorithms of chine learning accessible to stu-dents and nonexpert readers in statistics,computer science,thetics,and engineering.Shai Shalev-Shwartz is an Associate Professor at the School of ComputerScience and Engineering at The Hebrew University,Israel.Shai Ben-David is a Professor in the School of Computer Science at theUniversity of Waterloo,Canada.
