000			01567nam a22002297a 4500
008			231010b2023 |||||||| |||| 00| 0 eng d
020			_a9781944660345
082			_a512.50285 GAL-J
100			_aGallier, Jean
245			_aLinear algebra and optimization with applications to machine learning (Volume 1) : _blinear algebra for computer vision, robotics, and machine learning / _cJearn Gallier and Jocelyn Quaintance
260			_aIndia _bWorld Scientific _c2023
300			_a806p.
500			_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			_aRobotics
650			_aAlgebras, Linear--Data processing
650			_aComputer science--Mathematics
650			_aComputer-aided design
650			_aMachine learning
650			_aAlgebras, Linear
650			_aComputer vision
700			_aQuaintance, Jocelyn
999			_c90977 _d90977