000			01458nam a22002177a 4500
003			OSt
005			20250827161350.0
008			250827b |||||||| |||| 00| 0 eng d
020			_a9781944660345
040			_cAACR-II
082			_a005.1 GAL
100			_aGallier, Jean _913649
245			_aLinear algebra and optimization with applications to machine learning: Linear algebra for computer vision, robotics, and machine learning
260			_aSingapore _bWorld Scientific Publishing _c2023
300			_a806 p.
520			_aThis book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.
650			_aMachine Learning _913650
650			_a(Applied mathematics) _913651
700			_aQuaintance, Jocelyn _913652
942			_2ddc _cB
999			_c359209 _d359209