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list price: $60.00 USD
edition:Hardcover
also available: Paperback
category: Computers
published: Sep 2011
ISBN:9780262016469
publisher: The MIT Press

Optimization for Machine Learning

contributions by Suvrit Sra; Shie Mannor; Taiji Suzuki; Shiqian Ma; Selin Damla Ahipasaoglu; Sébsatien Bubeck; Jean-Yves Audibert; Rémi Munos; Masashi Sugiyama; Yoshua Bengio; Ryota Tomioka; Andrew Fitzgibbon; Zhang Liu; Arkadi Nemirovski; Martin Andersen; Sören Sonnenburg; Julien Mairal; David Sontag; Francis Bach; Tommi Jaakkola; Lieven Vandenberghe; Huan Xu; Constantine Caramanis; Jacek Gondzio; Stephen J. Wright; Sebastian Nowozin; Vijay Krishnamurthy; Elad Hazan; Mark Schmidt; Dongmin Kim; Katya Scheinberg; Léon Bottou; Olivier Bousquet; Nicolas Le Roux; Anatoli Juditsky; Dimitri Bertsekas; Vojtech Franc; Joachim Dahl; Thomá Werner; Guillaume Obozinski; Amir Globerson; Rodolphe Jenatton & Alexandre d'Aspremont

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intelligence (ai) & semantics, robotics
0 of 5
0 ratings
rated!
rated!
list price: $60.00 USD
edition:Hardcover
also available: Paperback
category: Computers
published: Sep 2011
ISBN:9780262016469
publisher: The MIT Press
Description

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.

The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields.
Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

About the Authors
Suvrit Sra is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tü bingen, Germany.
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Suvrit Sra is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tü bingen, Germany.
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Suvrit Sra is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tü bingen, Germany.
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Suvrit Sra is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tü bingen, Germany.
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Suvrit Sra is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tü bingen, Germany.
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Suvrit Sra is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tü bingen, Germany.
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Suvrit Sra is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tü bingen, Germany.
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Suvrit Sra is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tü bingen, Germany.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin– Madison.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Lé on Bottou is a Research Scientist at NEC Labs America.
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Lé on Bottou is a Research Scientist at NEC Labs America.
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Lé on Bottou is a Research Scientist at NEC Labs America.
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Lé on Bottou is a Research Scientist at NEC Labs America.
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Dimitri P. Bertsekas is Professor of Electrical Engineering and Computer Science at MIT.
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Dimitri P. Bertsekas is Professor of Electrical Engineering and Computer Science at MIT.
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Dimitri P. Bertsekas is Professor of Electrical Engineering and Computer Science at MIT.
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Dimitri P. Bertsekas is Professor of Electrical Engineering and Computer Science at MIT.
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Dimitri P. Bertsekas is Professor of Electrical Engineering and Computer Science at MIT.
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Dimitri P. Bertsekas is Professor of Electrical Engineering and Computer Science at MIT.
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Dimitri P. Bertsekas is Professor of Electrical Engineering and Computer Science at MIT.
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Dimitri P. Bertsekas is Professor of Electrical Engineering and Computer Science at MIT.
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Recommended Age, Grade, and Reading Levels
Age:
18 to 100
Grade:
13 to 17

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