Introduction to Applied Linear Algebra

Vectors, Matrices, and Least Squares

Nonfiction, Science & Nature, Technology, Engineering, Mathematics
Cover of the book Introduction to Applied Linear Algebra by Stephen Boyd, Lieven Vandenberghe, Cambridge University Press
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Author: Stephen Boyd, Lieven Vandenberghe ISBN: 9781108569613
Publisher: Cambridge University Press Publication: June 30, 2018
Imprint: Cambridge University Press Language: English
Author: Stephen Boyd, Lieven Vandenberghe
ISBN: 9781108569613
Publisher: Cambridge University Press
Publication: June 30, 2018
Imprint: Cambridge University Press
Language: English

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 at https://web.stanford.edu/~boyd/vmls/. 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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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 at https://web.stanford.edu/~boyd/vmls/. 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.

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