Optimization for learning and control / Anders Hansson, Linköping University, Linköping, Sweden; Martin Andersen, Technical University of Denmark, Kongens Lyngby, Denmark.

Includes index.

"Comprehensive resource providing a masters' level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems. Several applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. Today, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters' level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters' students in a coherent way"-- Provided by publisher.

About the Author
Anders Hansson, PhD, is a Professor in the Department of Electrical Engineering at Linköping University, Sweden. His research interests include the fields of optimal control, stochastic control, linear systems, signal processing, applications of control, and telecommunications.

Martin Andersen, PhD, is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark. His research interests include optimization, numerical methods, signal and image processing, and systems and control.

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