Matrix Algebra with Computational Applications by Dirk Colbry
Matrix Algebra with Computational Applications - Table of Contents
1. Matrix Algebra class preparation checklist
2. Welcome to Matrix Algebra with computational applications
3. Vectors
4. Vectors
5. Linear Equations
6. Solving Linear Systems of equations
7. Python Linear Algebra Packages
8. Linear Algebra and Python
9. Gauss-Jordan Elimination
10. Gauss-Jordan
11. Matrix Mechanics
12. Matrix Multiply
13. Transformation Matrix
14. Transformations
15. Robotics and Reference Frames
16. The Kinematics of Robotics
17. Determinants
18. Determinants 2
19. Eigenvectors and Eigenvalues
20. Eigenproblems
21. Vector Spaces
22. Vector Spaces 2
23. Matrix Spaces
24. Matrix Representation
25. Projections
26. Projections
27. Fundamental Spaces
28. Fundamental Spaces
29. Diagonalization and Powers
30. Diagonalization
31. Linear Dynamical Systems
32. Linear Dynamical Systems
33. Decompositions
34. Decompositions and Gaussian Elimination
35. Inner Product
36. Inner Products
37. Least Squares Fit (Regression)
38. Least Squares Fit (LSF)
39. Least Squares Fit (LSF)
40. Solve Linear Systems of Equations
41. Solve Linear Systems of Equations using QR Decomposition
42. Python Linear Algebra Packages
43. Jupyter Getting Started Guide
44. Python Linear Algebra Packages
What You Will Learn in Matrix Algebra with Computational Applications
"Matrix Algebra with Computational Applications" by "Dirk Colbry" is an applied textbook that teaches "matrix algebra" through practical computing and problem solving. Dirk Colbry focuses on using mathematics to solve real problems in science and engineering. Instead of emphasizing theory alone, the book integrates "computational applications" so students can learn how matrices and algorithms work in practice.
Key topics include "linear systems", matrix operations, and transformations, all demonstrated with hands-on examples. Students also use "Python programming" and computational tools to solve problems and visualize results. This approach helps learners understand both the mathematics and its applications in "data modeling" and scientific computation. The structured lessons and exercises support beginners while remaining useful for advanced study.
Overall, the textbook bridges algebra and computation, making it ideal for students in engineering, physics, and data science. By combining theory with practical coding, it prepares learners to apply "matrix algebra" in real-world situations. Whether used in classrooms or self-study, it provides a strong foundation in modern computational mathematics and problem solving.
Book Details & Specifications
Title:
Matrix Algebra with Computational Applications by Dirk Colbry
Publisher:
Michigan State University Libraries
Year:
2021
Pages:
110
Type:
PDF
Language:
English
ISBN-10 #:
3319648667
ISBN-13 #:
978-3319648668
License:
CC BY-NC 4.0
Amazon:
Amazon
About the Author: Dr. Dirk Colbry
The author Dr. Dirk Colbry
is faculty member in the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University (MSU). Dr. Colbry is an educator and researcher specializing in "matrix algebra" and "computational mathematics". He has decades of experience in curriculum development and teaching across a wide range of subjects, including: numerical linear algebra, parallel programming, microprocessors, artificial intelligence, scientific image analysis, compilers, GPU programming, next generation architectures, tools for computational modeling, algorithm analysis, and professional skills for scientists and engineers. In addition to teaching and curriculum development, Dr. Colbry is an expert in computer vision and scientific image understanding, and has collaborated on research in fields as diverse as Engineering, Toxicology, Plant and Soil Sciences, Zoology, Mathematics, Statistics, and Biology.
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