Understanding Machine Learning
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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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
La notice
- Revue
- Cambridge University Press eBooks
- Thématique
- Neural Networks and Applications
- Domaine
- Computer Science
- Établissements canadiens
- University of Waterloo
- Organismes subventionnaires
- —
- Mots-clés
- Computer scienceArtificial intelligenceStability (learning theory)Algorithmic learning theoryComputational learning theoryMachine learningPresentation (obstetrics)Stochastic gradient descentConvexityOnline machine learningArtificial neural network
- Résumé présent dans OpenAlex
- oui