000			01845nam a2200289Ia 4500
003			OSt
005			20251117124121.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9783030395674
037			_cTextual
040			_beng _aCSL _cCSL _dCSL
041			_aeng
084			_aB2811093 R0 _qCSL
100			_aLan, Guanghui _eauthor _9851646
245		0	_aFirst-order and stochastic optimization methods for machine learning
260			_aSwitzerland, _bSpringer: _c2020
300			_axiii, 582p. _b: ill.
490			_aSpringer series in the data sciences _x2365-5674
500			_a References 567-575p.; Index 577-582p.
520			_aThis book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
650			_a Machine learning models _9851647
650			_a Noncovex optimization _9851648
650			_a Stochastic convex optimization _9851649
650			_aOperational Research _9851650
942			_hB2811093 R0 _cTEXL _2CC _n0
999			_c4503 _d4503