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: TextTextLanguage: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
Item type Current location Collection Call number Status Date due Barcode Item holds
Book Book Books at groups
Mondelli Group Not for loan
Book Book Library
006 (Browse shelf) Checked out 22/12/2020 AT-ISTA#001740
Book Book Library
006 (Browse shelf) Available AT-ISTA#001683
Book Book Library
006 (Browse shelf) Available AT-ISTA#001671
Book Book Library
006 (Browse shelf) Checked out 31/12/2020 AT-ISTA#001368
Book Book Library
006 (Browse shelf) Checked out 12/12/2020 AT-ISTA#000598
Reference Book Reference Book Library
006 (Browse shelf) Available AT-ISTA#000077
Total holds: 0

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.

There are no comments for this item.

to post a comment.

Powered by Koha