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Understanding Machine Learning: From Theory To Algorithms

2015· book· en· 3 092 citations· W607505555 sur OpenAlex· 10.1017/cbo9781107298019

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Résumé

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

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La notice

Revue
Thématique
Machine Learning and Algorithms
Domaine
Computer Science
Établissements canadiens
University of Waterloo
Organismes subventionnaires
Mots-clés
Computer scienceArtificial intelligenceField (mathematics)Machine learningComputational learning theoryStability (learning theory)Algorithmic learning theoryStochastic gradient descentAlgorithmPresentation (obstetrics)ConvexityOnline machine learningArtificial neural networkMathematics
Résumé présent dans OpenAlex
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