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020			_a9789811206405 _q(ebook)
020			_a9811206406 _q(ebook)
020			_z9811206392 (hbk.)
020			_z9789811206399 (hbk.)
020			_z9789811207716 (pbk.)
020			_z9811207712 (pbk.)
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050	0	4	_aQA185.D37 _bG35 2020
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082	0	4	_a512.50285 _223
100	1		_aGallier, Jean H. _94003
245	1	0	_aLinear algebra and optimization with applications to machine learning. _nVolume 1, _pLinear algebra for computer vision, robotics, and machine learning _h[electronic resource] / _cJean Gallier, Jocelyn Qqaintance.
246	1	0	_aLinear algebra for computer vision, robotics, and machine learning
260			_aSingapore : _bWorld Scientific Publishing Co. Pte. Ltd., _cc2020.
300			_a1 online resource (824 p.)
538			_aSystem requirements: Adobe Acrobat reader.
538			_aMode of access: World Wide Web.
504			_aIncludes bibliographical references and index.
505	0		_ach. 1. Introduction -- ch. 2. Vector spaces, bases, linear maps -- ch. 3. Matrices and linear maps -- ch. 4. Haar bases, haar wavelets, hadamard matrices -- ch. 5. Direct sums, rank-nullity theorem, affine maps -- ch. 6. Determinants -- ch. 7. Gaussian elimination, LU-factorization, Cholesky factorization, reduced row echelon form -- ch. 8. Vector norms and matrix norms -- ch. 9. Iterative methods for solving linear systems -- ch. 10. The dual space and duality -- ch. 11. Euclidean spaces -- ch. 12. QR-decomposition for arbitrary matrices -- ch. 13. Hermitian spaces -- ch. 14. Eigenvectors and eigenvalues -- ch. 15. Unit quaternions and rotations in SO(3) -- ch. 16. Spectral theorems in euclidean and hermitian spaces -- ch. 17. Computing eigenvalues and eigenvectors -- ch. 18. Graphs and graph laplacians; basic facts -- ch. 19. Spectral graph drawing -- ch. 20. Singular value decomposition and polar form -- ch. 21. Applications of SVD and pseudo-inverses -- ch. 22. Annihilating polynomials and the primary decomposition -- Bibliography -- Index.
520			_a"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"--Publisher's website.
650		0	_aAlgebras, Linear _xData processing. _94024
650		0	_aComputer science _xMathematics. _93866
650		0	_aComputer-aided design. _93932
650		0	_aRobotics. _92393
655		0	_aElectronic books. _93294
700	1		_aQuaintance, Jocelyn. _94006
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/11446#t=toc _zAccess to full text is restricted to subscribers.
942			_cEBK
999			_c72583 _d72583