01724nam 22002417a 4500008004500000020001800045082002000063100001800083245019500101260003400296300001000330500078300340650001301123650003801136650003401174650002601208650002101234650002101255650002001276700002401296952014501320999001701465231010b2023 |||||||| |||| 00| 0 eng d a9781944660345 a512.50285 GAL-J aGallier, Jean 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 aIndiabWorld Scientificc2023 a806p. 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 aRobotics aAlgebras, Linear--Data processing aComputer science--Mathematics aComputer-aided design aMachine learning aAlgebras, Linear aComputer vision aQuaintance, Jocelyn 00102ddc406512_502850000000000_GALJ708MATH9107236aBITHbBITHcGENd2023-10-10l0o512.50285 GAL-Jp47345r2023-10-10w2023-10-10yBKK c90977d90977