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Understanding Machine Learning
Understanding Machine Learning
Understanding Machine Learning
Understanding Machine Learning

Understanding Machine Learning (From Theory to Algorithms)

Product ID : 14399552
4.1 out of 5 stars


Galleon Product ID 14399552
UPC / ISBN 1107057132
Shipping Weight 2 lbs
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Binding: Hardcover
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Model
Manufacturer Cambridge University Press
Shipping Dimension 10.24 x 7.48 x 0.98 inches
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Edition 1
Number Of Pages 410
Package Quantity 1
Publication Date 2014-05-19
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About Understanding Machine Learning

Product Description 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. Review "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 "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 Book Description Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage. About the Author Shai Shalev-Shwartz is an Associate Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel. Shai Ben-David is a Professor in the School of Computer Science at the University of Waterloo, Canada.