"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
TYPE OF ELECTRONIC RESOURCE NOTE
Text of Note
PDF file.
TOPICAL NAME USED AS SUBJECT
Entry Element
Mathematical optimization
Entry Element
Signal processing
Topical Subdivision
-- Mathematics
a06
a09
MATLAB.
System analysis -- Mathematics.
Machine learning -- Mathematics.
PERSONAL NAME - PRIMARY RESPONSIBILITY
Entry Element
Hansson
Part of Name Other than Entry Element
, Anders
a01
ba
PERSONAL NAME - ALTERNATIVE RESPONSIBILITY
Andersen, Martin S.
ORIGINATING SOURCE
Country
Iran
Agency
University of Tehran. Library of College of Science