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Einführung in die angewandte lineare Algebra: Vektoren, Matrizen und kleinste Quadrate-
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eBay-Artikelnr.:167601258474
Artikelmerkmale
- Artikelzustand
- ISBN
- 9781316518960
Über dieses Produkt
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1316518965
ISBN-13
9781316518960
eBay Product ID (ePID)
242837315
Product Key Features
Number of Pages
474 Pages
Language
English
Publication Name
Introduction to Applied Linear Algebra : Vectors, Matrices, and Least Squares
Subject
Engineering (General), Algebra / Linear, Probability & Statistics / General, Matrices
Publication Year
2018
Type
Textbook
Subject Area
Mathematics, Technology & Engineering
Format
Hardcover
Dimensions
Item Height
1 in
Item Weight
41.6 Oz
Item Length
10 in
Item Width
7.7 in
Additional Product Features
Intended Audience
College Audience
LCCN
2018-017123
Dewey Edition
23
Reviews
Advance praise: 'This book explains the least squares method and the linear algebra it depends on - and the authors do it right!' Gilbert Strang, Massachusetts Institute of Technology
Illustrated
Yes
Dewey Decimal
512.5
Table Of Content
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.
Synopsis
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(R), 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., A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance., 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.
LC Classification Number
QA184.2
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