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Introduction to applied linear algebra: vectors, matrices and least squares

By: Boyd, Stephen.
Contributor(s): Vandenberghe, Lieven [Co author].
Material type: materialTypeLabelBookPublisher: Cambridge Cambridge University Press 2018Description: xii, 463 p. Includes index.ISBN: 9781316518960.Subject(s): Matrices | Vector algebra | Algebras - Linear | Least squaresDDC classification: 512.5 Summary: Description Contents Resources Courses About the Authors 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. https://www.cambridge.org/gb/academic/subjects/engineering/engineering-mathematics-and-programming/introduction-applied-linear-algebra-vectors-matrices-and-least-squares?format=HB
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Item type Current location Collection Call number Status Date due Barcode
Books Vikram Sarabhai Library
General Stacks
Non-fiction 512.5 B6I6 (Browse shelf) Available 199668

Table of contents:

Vectors --
Linear functions --
Norm and distance --
Clustering --
Linear independence --
Matrices --
Matrix examples --
Linear equations --
Linear dynamical systems --
Matrix multiplication --
Matrix inverses --
Least squares --
Least squares data fitting --
Least squares classification --
Multi-objective least squares --
Constrained least squares --
Constrained least squares applications --
Nonlinear least squares --
Constrained nonlinear least squares.

Description Contents Resources Courses About the Authors
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.

https://www.cambridge.org/gb/academic/subjects/engineering/engineering-mathematics-and-programming/introduction-applied-linear-algebra-vectors-matrices-and-least-squares?format=HB

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