000			01903nam a2200229 4500
005			20250802145118.0
008			250802b2023|||||||| |||| 00| 0 eng d
020			_a9781119809142
041			_aEnglish
082			_a004
100			_aAndersen, Martin _eAuthor _97199
100			_aHansson, Anders _eCo-Author _97200
245			_aOptimization for learning and control
260			_aNew Jersey: _bWiley Data and Cybersecurity, _c2023.
300			_axxvii, 397p.
500			_aOptimization for Learning and Control 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. The focus is on basic algorithmic principles and trade-offs.
650			_a Linear Algebra and Probability Theory _97201
650			_aOptimization Theory and Optimization Problems _97202
650			_aCalculus of Variations and Dynamic Programming _97203
856			_uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=10132902
942			_cEB
999			_c1776 _d1776