Understanding Linear Algebra by David Austin
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About this book :-
Understanding Linear Algebra is a freely available linear algebra textbook suitable for use in a first undergraduate linear algebra course. The text aims to support readers as they develop their ability to reason mathematically, their computational fluency, and their understanding of the role that linear algebra plays in shaping our society. It is also designed to support an active learning classroom environment.
Understanding Linear Algebra is an open textbook designed to support a two-course undergraduate linear algebra sequence. Topics include systems of equations, vector and matrix algebra, span, linear independence, bases, eigenvectors and eigenvalues, orthogonality, least squares, and singular value decompositions.
Until recently, linear algebra has mainly lived in the long shadow of calculus with many university linear algebra courses requiring several semesters of calculus as a prerequisite. Given the increasing prominence of linear algebra, Understanding Linear Algebra assumes no familiarity with calculus and, as such, can provide an alternative introduction into university-level mathematics. This book arises from my belief that linear algebra, as presented in a traditional undergraduate curriculum, has for too long lived in the shadow of calculus. Many mathematics programs currently require their students to complete at least three semesters of calculus, but only one semester of linear algebra, which often has two semesters of calculus as a prerequisite.
Book Detail :-
This book has following details information.
Title:
Understanding Linear Algebra by David Austin by NA
Publisher:
Wreath
Series:
freeCompBooks
Year:
2023
Pages:
517
Type:
PDF
Language:
English
ISBN-10 #:
1958469165
ISBN-13 #:
978-1958469163
Country:
Pakistan
License:
Get this book from Amazon
About Author :-
The author NA
NA
Book Contents :-
conver the following topics.
1. Systems of equations
2. Vectors, matrices, and linear combinations
3. Invertibility, bases, and coordinate systems
4. Eigenvalues and eigenvectors
5. Linear algebra and computing
6. Orthogonality and Least Squares
7. Singular value decompositions
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