Understanding Machine Learning: From Theory to Algorithms


Understanding Machine Learning: From Theory to Algorithms

Bücher Details

  • Titel:Understanding Machine Learning: From Theory to Algorithms
  • Dateiname: understanding-machine-learning-from-theory-to-algorithms.pdf
  • ISBN: 1017561107057132
  • Datum des Hochladens: 2020-01-15
  • Anzahl der Seiten: 667 Seiten
  • Autor: Shai Shalev-Shwartz/Shai Ben-David
  • Verlag: Shai Shalev-Shwartz/Shai Ben-David

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Überblick:

Pressestimmen 'This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource ideal for all those who want to understand how to find structure in data.' Bernhard Schölkopf Max Planck Institute for Intelligent Systems Germany'This is a timely text on the mathematical foundations of machine learning providing a treatment that is both deep and broad not only rigorous but also with intuition and insight. It presents a wide range of classic fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great book for anyone interested in the mathematical and computational underpinnings of this important and fascinating field.' Avrim Blum Carnegie Mellon University'This text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area it covers the range from theoretical foundations to algorithms at a level appropriate for an advanced undergraduate course.' Peter L. Bartlett University of California Berkeley Über das Produkt Machine learning is one of the fastest growing areas of computer science with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the 'hows' and 'whys' of machine-learning algorithms making the field accessible to both students and practitioners. Alle Produktbeschreibungen

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