Linear Algebra – Foundations to Frontiers

About this course

Linear Algebra: Foundations to Frontiers (LAFF) is packed full of challenging, rewarding material that is essential for mathematicians, engineers, scientists, and anyone working with large datasets. Students appreciate our unique approach to teaching linear algebra because:

  • It’s visual.
  • It connects hand calculations, mathematical abstractions, and computer programming.
  • It illustrates the development of mathematical theory.
  • It’s applicable.

In this course, you will learn all the standard topics that are taught in typical undergraduate linear algebra courses all over the world, but using our unique method, you’ll also get more! LAFF was developed following the syllabus of an introductory linear algebra course at The University of Texas at Austin taught by Professor Robert van de Geijn, an expert on high performance linear algebra libraries. Through short videos, exercises, visualizations, and programming assignments, you will study Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear Least-Squares, and Eigenvalues and Eigenvectors. In addition, you will get a glimpse of cutting edge research on the development of linear algebra libraries, which are used throughout computational science.

MATLAB licenses will be made available to the participants free of charge for the duration of the course.

Pre-releases of LAFF start August 3 for those who want a review before their fall courses begin. Each of three pre-releases will consist of four ”Weeks” plus an exam. The course officially begins on August 24 with suggested weekly due dates. However, only December 14, when the “Course Closes,” is a true deadline.

We invite you to LAFF with us!


What you’ll learn

  • Connections between linear transformations, matrices, and systems of linear equations
  • Partitioned matrices and characteristics of special matrices
  • Algorithms for matrix computations and solving systems of equations
  • Vector spaces, subspaces, and characterizations of linear independence
  • Orthogonality, linear least-squares, eigenvalues and eigenvectors

View Course Syllabus

Meet the instructors

  • bio for Maggie Myers

    Maggie Myers

    Lecturer, Department of Statistics and Data SciencesThe University of Texas at Austin

  • bio for Robert van de Geijn

    Robert van de Geijn

    Professor of Computer ScienceThe University of Texas at Austin

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