Linear Algebra for Computer Vision & ML by Jean Gallier
Linear Algebra for Computer Vision & ML - Table of Contents
- 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, Cholesky, Echelon Form
- 8. Vector Norms and Matrix Norms
- 9. Iterative 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
- 17. Computing Eigenvalues and Eigenvectors
- 18. Graphs and Graph Laplacians
- 19. Spectral Graph Drawing
- 20. Singular Value Decomposition and Polar Form
- 21. Applications of SVD and Pseudo-Inverses
- 22. Annihilating Polynomials; Primary Decomposition
What You Will Learn in Linear Algebra for Computer Vision & ML
Linear Algebra for Computer Vision, Robotics, and Machine Learning by Jean Gallier and Jocelyn Quaintance is an essential, comprehensive treatise designed for researchers, computer scientists, and AI engineers. Recognized globally as a fundamental reference for linear algebra for computer vision, robotics, and machine learning, this text bridges rigorous linear analysis with modern computational applications.
The book systematically covers vector spaces, linear transformations, matrix factorizations, Singular Value Decomposition (SVD), Principal Component Analysis (PCA), least squares optimization, convex geometry, and quadratic optimization. Studying the linear algebra for computer vision robotics and machine learning Jean Gallier pdf equips engineers with mathematical foundations necessary to solve complex problems in computer vision algorithms, 3D kinematics, and machine learning models.
Praised for its depth and clarity, this textbook connects pure mathematical structures with real-world computational implementations. Mastering linear algebra for computer vision, robotics, and machine learning provides the core analytical and geometric tools required for advanced artificial intelligence research.
Book Details & Specifications
Title:
Linear Algebra for Computer Vision & ML by Jean Gallier
Publisher:
University of Pennsylvania
Year:
2024
Pages:
787
Type:
PDF
Language:
English
ISBN-10 #:
9811206392
ISBN-13 #:
978-9811206399
License:
External Educational Resource
Amazon:
Amazon
About the Author: Jean Gallier
The author Jean Gallier
is Professor Emeritus of Computer and Information Science at the University of Pennsylvania, widely recognized for his research in logic, geometric modeling, and linear algebra applications in computer science.
Jocelyn Quaintance is a Senior Lecturer at the University of Pennsylvania, specializing in combinatorics, mathematical physics, and educational resources in linear algebra for computer vision, robotics, and machine learning.
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