Understanding machine learning from theory to algorithms Shai Shalev-Shwartz (The Hebrew University, Jerusalem), Shai Ben-David (University of Waterloo, Canada)
By: Shalev-Shwartz, Shai [VerfasserIn]
Contributor(s): Ben-David, Shai [VerfasserIn]
Material type: 
TextLanguage: English Publisher: Cambridge New York, NY; Port Melbourne Delhi Singapore Cambrige University Press [2014]Description: xvi, 397 Seiten IllustrationenContent type: Text Media type: ohne Hilfsmittel zu benutzen Carrier type: BandISBN: 9781107057135 (hardback)Subject(s): Maschinelles Lernen | Machine learning | AlgorithmsAdditional physical formats: Online-Ausg.: Shalev-Shwartz, Shai: Understanding machine learningDDC classification: 006.3/1 LOC classification: Q325.5Other classification: 54.72 | ST 302 | ST 300 | ST 301 | mat Online resources: Zentralblatt MATH | table of content Summary: 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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Literaturverzeichnis: Seite 385-393
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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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