000			01597 a2200193 4500
003			OSt
005			20250624121542.0
008			250624b |||||||| |||| 00| 0 eng d
020			_a9781119809135
082			_a004.10151 _bH199o
100			_aHansson, Anders
245			_aOptimization for learning and control _cAnders Hansson and Martin Andersen
260			_bJohn Wiley _c2023 _aHoboken
300			_axxvii, 397p
520			_aOptimization 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			_aSystem analysis -- Mathematics _aMathematical optimization _aMachine learning -- Mathematics _aSignal processing -- Mathematics _aMATLAB
700			_aAndersen, Martin
942			_cBK
999			_c567518 _d567518