| Description |
1 online resource (202 p.) |
| Series |
Compact Textbooks in Mathematics |
|
Compact textbooks in mathematics.
|
| Contents |
Intro -- Preface -- Contents -- 1 Introduction -- References -- 2 Projections -- Exercises -- References -- 3 Matrix Algebra -- Exercises -- Reference -- 4 Rotations and Quaternions -- Exercises -- References -- 5 Haar Wavelets -- Exercises -- References -- 6 Singular Value Decomposition -- Exercises -- References -- 7 Convolution -- Exercises -- References -- 8 Frequency Filtering -- Exercises -- References -- 9 Neural Networks -- References -- 10 Some Wavelet Transforms -- References -- A Appendix -- Vectors -- Exercises -- Matrices -- Exercises |
| Summary |
This textbook explores applications of linear algebra in data science at an introductory level, showing readers how the two are deeply connected. The authors accomplish this by offering exercises that escalate in complexity, many of which incorporate MATLAB. Practice projects appear as well for students to better understand the real-world applications of the material covered in a standard linear algebra course. Some topics covered include singular value decomposition, convolution, frequency filtering, and neural networks. Linear Algebra in Data Science is suitable as a supplement to a standard linear algebra course |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed May 22, 2024) |
| Subject |
Algebras, Linear.
|
| Form |
Electronic book
|
| Author |
La Haye, Roberta
|
| ISBN |
9783031549083 |
|
3031549082 |
|
