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

By:

Boyd, Stephen

Contributor(s):

Vandenberghe, Lieven

Publication details: Cambridge University Press, 2018. Cambridge:Description: xii, 463 p.: ill.; hb; 26 cmISBN:

9781316518960

Subject(s):

DDC classification:

512.5 BOY

Summary: 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.

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	IIT Gandhinagar General Stacks	General	512.5 BOY (Browse shelf(Opens below))	1	Available		029497

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Previous	512.482 KIR Introduction to lie groups and lie algebras	512.482 MAL Linear algebraic groups and finite groups of lie type	512.5 BAP Graphs and matrices	512.5 BOY Introduction to applied linear algebra: vectors, matrices, and least squares	512.5 COL Dynamical systems and linear algebra	512.5 DYM Linear algebra in action	512.5 GRA Extension theory	Next

Includes bibliographical references and index.

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.

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