Linear Algebra And Optimization With Applications To Machine Learning - Volume I: Linear Algebra For Computer Vision, Robotics, And Machine Learning
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A01=Jean H Gallier
A01=Jocelyn Quaintance
Adjoint
Affine Maps
Author_Jean H Gallier
Author_Jocelyn Quaintance
Basis
Category=PBF
Category=UYAM
Category=UYQM
Category=UYQV
Cholesky
Curve Interpolation
Determinants
Dual Space
Eigenvalues
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eq_computing
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eq_isMigrated=2
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Euclidean Spaces
Graph Laplacian
Haar Wavelets
Hermitian Spaces
Inner Product
Kernels
Least Squares Problem
Linear Maps
LU
Matrix Exponential
Matrix Norms
Orthogonal Group
PCA (Principal Component Analysis)
Primary Decomposition Theorem
Pseudo-Inverse
QR-Decomposition
Quaternions
Rref
Spectral Graph Theorem
SVD
Vectors
Product details
- ISBN 9789811206399
- Publication Date: 07 Feb 2020
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
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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.
