MARC details
| 000 -LEADER |
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| fixed length control field |
02332 a2200205 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20241025163448.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240228b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781944660345 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
512.5 |
| Item number |
GAL |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gallier, Jean |
| 9 (RLIN) |
13541 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Linear algebra and optimization with applications to machine learning |
| Remainder of title |
: linear algebra for computer vision, robotics, and machine learning- Vol. 1 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Name of publisher, distributor, etc. |
World Scientific |
| Date of publication, distribution, etc. |
2023. |
| Place of publication, distribution, etc. |
Singapore |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
806 p. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
1. Introduction 2. Vector Spaces, Bases, Linear Maps 3. Matrices and Linear Maps 4. Haar Bases, Haar Wavelets, Hadamard Matrices 5. Direct Sums, Rank-Nullity Theorem, Affine Maps 6. Determinants 7. Gaussian Elimination, LU-Factorization, Cholesky Factorization, Reduced Row Echelon Form 8. Vector Norms and Matrix Norms 9. Iteractive Methods for Solving Linear Systems 10. The Dual Space and Duality 11. Euclidean Spaces 12. QR-Decomposition for Arbitrary Matrices 13. Hermitian Spaces 14. Eigenvectors and Eigenvalues 15. Unit Quaternions and Rotations in SO(3) 16. Spectral Theorems in Euclidean and Hermitian Spaces 17. Computing Eigenvalues and Eigenvectors 18. Graphs and Graph Laplacians; Basic Facts 19. Spectral Graph Drawing 20. Singular Value Decomposition and Polar Form 21. Applications of SVD and Pseudo-Inverses 22. Annihilating Polynomials and the Primary Decomposition |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Algebras, Linear |
| 9 (RLIN) |
16135 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine learning--Mathematics. |
| 9 (RLIN) |
16136 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Jocelyn, Quaintance |
| 9 (RLIN) |
15214 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |
