000 01925cam a22002658i 4500
005 20251223112615.0
008 230124s2023 nju o 001 0 eng
020 _a9781119809173
020 _a9781119809142
041 _aeng
082 0 0 _a004.2/10151
100 1 _aHansson, Anders,
245 1 0 _aOptimization for learning and control /
_cAnders Hansson, Linköping University, Linköping, Sweden; Martin Andersen, Technical University of Denmark, Kongens Lyngby, Denmark.
250 _aFirst edition.
300 _a1 online resource
500 _aIncludes index.
520 _a"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"--
650 0 _aSystem analysis
_xMathematics.
650 0 _aMathematical optimization.
650 0 _aMachine learning
_xMathematics.
650 0 _aSignal processing
_xMathematics.
700 1 _aAndersen, Martin S.,
856 _uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=10132902
942 _cEBK
999 _c7814
_d7814