Understanding Linear Algebra - David Austin - Bog

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.

There are a few features that distinguish it from other linear algebra textbooks. First, it will always be freely available at http://gvsu.edu/s/0Ck in a number of formats, including accessible HTML, PDF, and even braille.

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.

Learners are supported as they develop a deep understanding of linear algebraic concepts and their ability to reason mathematically using those concepts. While not intended as an introduction to proofs, the text helps learners to express their thinking clearly and with precision. Following best pedagogical practices, numerous activities are interwoven with exposition to facilitate active learning and can be easily adapted for small group collaboration in a classroom. Each section begins with a preview activity to support a flipped class environment and concludes with numerous exercises of varying depth.

In addition, learners develop computational proficiency through the use of Sage, an open source computer algebra system. The online version of the text contains many embedded "Sage cells" that enable readers to perform computations directly in the book as they are reading. Readers first perform basic algorithms, such as Gaussian elimination and matrix multiplication, by hand but later automate them using Sage. In this way, learners can focus on higher-level linear algebraic thinking and develop their ability to deploy it in more realistic situations.

By introducing many in-depth applications, Understanding Linear Algebra also aims to develop an appreciation for the many significant ways in which linear algebra impacts our society. Examples include the JPEG image compression and Google's PageRank algorithms as well as important data science topics such as k-means clustering, linear regression, principal component analysis, and singular value decompositions. These applications give concrete meaning to many of the abstract algebraic concepts on which they rely, and the use of Sage enables learners to authentically explore them.

Besides the text itself, there is an accompanying workbook that contains the activities and is suitable for in-class use. There are also solution manuals for both the activities and the homework exercises that are available upon request of the author and a community of instructors who share their experiences and resources with one another through a Google Group.