Shalev-Shwartz, Shai

Understanding machine learning from theory to algorithms Shai Shalev-Shwartz (The Hebrew University, Jerusalem), Shai Ben-David (University of Waterloo, Canada) - xvi, 397 Seiten Illustrationen

Literaturverzeichnis: Seite 385-393 Hier auch später erschienene, unveränderte Nachdrucke

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.

9781107057135 (hardback) USD 60.00

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Machine learning



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