Linear algebra and optimization with applications to machine learning :
Gallier, Jean
Linear algebra and optimization with applications to machine learning : linear algebra for computer vision, robotics, and machine learning, vol I by Jean Gallier and Jocelyn Quaintance - Singapore : World Scientific, ©2023 - xv, 806 p. : ill. ; 23 cm - Linear algebra and optimization with applications to machine learning ; 1 .
Includes bibliographical references and index.
Volume I. Linear algebra for computer vision, robotics, and machine learning -- Volume II. Fundamentals of optimization theory with applications to machine learning
"This 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"--
9781944660345
2020001727
Algebras, Linear.
Machine learning--Mathematics.
Robotics
Algebras, Linear -- Data processing
Computer science -- Mathematics
Computer - aided design
Machine learning
Algebras, Linear
Computer vision
QA184.2 / .G35 2020
512.502 / GAL-L
Linear algebra and optimization with applications to machine learning : linear algebra for computer vision, robotics, and machine learning, vol I by Jean Gallier and Jocelyn Quaintance - Singapore : World Scientific, ©2023 - xv, 806 p. : ill. ; 23 cm - Linear algebra and optimization with applications to machine learning ; 1 .
Includes bibliographical references and index.
Volume I. Linear algebra for computer vision, robotics, and machine learning -- Volume II. Fundamentals of optimization theory with applications to machine learning
"This 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"--
9781944660345
2020001727
Algebras, Linear.
Machine learning--Mathematics.
Robotics
Algebras, Linear -- Data processing
Computer science -- Mathematics
Computer - aided design
Machine learning
Algebras, Linear
Computer vision
QA184.2 / .G35 2020
512.502 / GAL-L