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Full Description
This groundbreaking textbook combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least squares - that are needed for engineering applications, discussing examples across data science, machine learning and artificial intelligence, signal and image processing, tomography, navigation, control, and finance. The numerous practical exercises throughout allow students to test their understanding and translate their knowledge into solving real-world problems, with lecture slides, additional computational exercises in Julia and MATLAB®, and data sets accompanying the book online. Suitable for both one-semester and one-quarter courses, as well as self-study, this self-contained text provides beginning students with the foundation they need to progress to more advanced study.
Contents
Part I. Vectors: 1. Vectors; 2. Linear functions; 3. Norm and distance; 4. Clustering; 5. Linear independence; Part II. Matrices: 6. Matrices; 7. Matrix examples; 8. Linear equations; 9. Linear dynamical systems; 10. Matrix multiplication; 11. Matrix inverses; Part III. Least Squares: 12. Least squares; 13. Least squares data fitting; 14. Least squares classification; 15. Multi-objective least squares; 16. Constrained least squares; 17. Constrained least squares applications; 18. Nonlinear least squares; 19. Constrained nonlinear least squares; Appendix A; Appendix B; Appendix C; Appendix D; Index.
