Notes for Computational Linear Algebra by Jessy Grizzle
Notes for Computational Linear Algebra - Table of Contents
- 1. Introduction to Systems of Linear Equations
- 2. Vectors, Matrices, and Determinants
- 3. Triangular Systems: Forward & Back Substitution
- 4. Matrix Multiplication
- 5. LU (Lower-Upper) Factorization
- 6. Determinants, Inverses, and Transposes
- 7. The Vector Space Rn: Part 1
- 8. Least Squared Error & Linear Regression
- 9. The Vector Space Rn: Part 2
- 10. Solutions of Nonlinear Equations
- 11. Basic Ideas of Optimization
- A. Background for Machine Learning
- B. Important Omitted Topics
- C. Ordinary Differential Equations
- D. Camera and LiDAR Models for Robotics
What You Will Learn in Notes for Computational Linear Algebra
Linear Algebra and Computational Methods is an authoritative, mathematically rigorous textbook that introduces undergraduate students, data scientists, and engineers to the essential matrix algebra and numerical methods underlying modern computational science. This comprehensive volume guides readers through systems of linear equations, LU factorization, determinant identities, vector spaces, Euclidean norm projections, least squares linear regression, root-finding for nonlinear equations, numerical optimization, and differential equations with exceptional clarity and structural logic.
Designed for computer science majors, machine learning engineers, robotics specialists, and applied mathematicians, this foundational text bridges abstract vector space theory directly with practical engineering algorithms. The author details step-by-step algorithms for forward and back substitution, matrix inverse computations, objective function minimization, and camera/LiDAR geometry transformations. Whether you are analyzing high-dimensional data, building machine learning models, or implementing numerical solvers, this book offers an indispensable roadmap.
Recognized globally for its applied focus and integration of machine learning prerequisites, it stands as one of the best introductory books on applied linear algebra and numerical methods. By connecting matrix operations directly to optimization and modern robotics sensing models, it serves as an essential tool for mastering linear algebra and computational mathematics with confidence.
Book Details & Specifications
Title:
Notes for Computational Linear Algebra by Jessy Grizzle
Publisher:
University of Michigan
Year:
2020
Pages:
282
Type:
PDF
Language:
English
ISBN-10 #:
B08PW3YH4P
ISBN-13 #:
978-1482258714
License:
External Educational Resource
Amazon:
Amazon
About the Author: Jessy W. Grizzle
The author Jessy W. Grizzle
is a distinguished professor and researcher specializing in applied mathematics, numerical analysis, and computational engineering, widely recognized for developing accessible curricula that connect linear algebra to modern machine learning and robotics.
Through Linear Algebra and Computational Methods, the author delivers a clear, highly applicable approach to matrix theory and numerical algorithms. The book's detailed step-by-step derivations, practical computing models, and foundational machine learning insights make this text a classic self-study reference for linear algebra and computational science trusted by students and professionals worldwide.
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