UTAustinX: UT.5.01x: Linear Algebra  Foundations to Frontiers
Prerequisites:
High School Algebra, Geometry, and PreCalculus.
Linear Algebra  Foundations to Frontiers
Learn the theory of linear algebra handinhand with the practice of software library development.
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 LeastSquares, 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.
We invite you to LAFF with us!
Ways to take this edX course:
Simply Audit this Course
Audit this course for free and have complete access to all of the course material, tests, and the online discussion forum. You decide what and how much you want to do.
Pursue a Verified Certificate of Achievement
Plan to use your completed coursework for job applications, promotions or school applications? Then you may prefer to work towards a verified Certificate of Achievement to document your accomplishment.
Course Staff

Maggie Myers
Dr. Maggie Myers is a lecturer for the Department of Computer Science and Division of Statistics and Scientific Computing. She currently teaches undergraduate and graduate courses in Bayesian Statistics. Her research activities range from informal learning opportunities in mathematics education to formal derivation of linear algebra algorithms. Earlier in her career she was a senior research scientist with the Charles A. Dana Center and consultant to the Southwest Educational Development Lab (SEDL). Her partnerships (in marriage and research) with Prof. van de Geijn have lasted for decades and seem to be surviving the development of this MOOC.

Robert van de Geijn
With a Ph.D. in applied mathematics, Robert van de Geijn is a professor of Computer Science and a member of the Institute for Computational Engineering and Sciences and the Division of Statistics and Scientific Computation at the University of Texas at Austin. Prof. van de Geijn is a leading expert in the areas of highperformance computing, linear algebra libraries, parallel processing, and formal derivation of algorithms. He is the recipient of the 20072008 President’s Associates Teaching Excellence Award from The University of Texas at Austin.
Prerequisites
High School Algebra, Geometry, and PreCalculus.
FAQs
What is the estimated effort for the course?
About 8 hrs/week.
How much does it cost to take the course?
Nothing! The course is free.
Will the text for the videos be available?
Yes. All of our videos will have transcripts synced to the videos.
Are notes available for download?
PDF of notes will be available for free download.
Do I need to watch the videos live?
No. You watch the videos at your leisure.
Can I contact the Instructor or Teaching Assistants?
Yes, but not directly. The discussion forums are the appropriate venue for questions about the course. The instructors will monitor the discussion forums and try to respond to the most important questions; in many cases response from other students and peers will be adequate and faster.
Is this course related to a campus course of The University of Texas at Austin?
Yes. This course corresponds to the Division of Statistics and Scientific Computing titled “SSC329C: Practical Linear Algebra”, one option for satisfying the linear algebra requirement for the undergraduate degree in computer science.
Is there a certificate available for completion of this course?
Online learners who successfully complete LAFF can obtain an edX certificate. This certificate indicates that you have successfully completed the course, but does not include a grade.
Must I work every problem correctly to receive the certificate?
No, you are neither required nor expected to complete every problem.
What textbook do I need for the course?
None, PDF versions of our notes will be available for free download.
What are the principles by which assignment due dates are established?
There is a window of 20 days between the material release and the due date for the homework of that week. While we encourage you to complete a week’s work before the launch of the next week, we realize that life sometimes gets in the way so we have established a flexible cushion. Please don’t procrastinate. The course closes 12 May 2014. This is to give you twenty days from the release of the final to complete the course.
What software do I need for the course?
We will utilize VirtualBox, Vagrant, and Git, which are available for free. We have configured a virtual machine for download to ensure that all participants have the same software and environment. With it, you will create a small linear algebra package using Python 3 and iPython Notebooks. Detailed, easy instructions will explain how to download, install, and use the software. If you are registered for the course, you will receive an email alerting you when these instructions become available. You will be able to access them at least a week before the course begins. (Don't be intimidated by the jargon. We'll get you through it.)
Are there any special system requirements?
You may need at least 768MB of RAM memory and 24GB of free hard drive space. You should be able to successfully access the course using Chrome and Firefox.