EE103 is a new course that was taught for the first time Autumn quarter 2014–15. It covers the basics of matrices and vectors, solving linear equations, least-squares methods, and many applications. We'll cover the mathematics, but the focus will be on using matrix methods in applications such as tomography, image processing, data fitting, time series prediction, finance, and many others. Eventually, the course will be suitable for any undergraduate; but for the first few offerings, while we work out the bugs, we are targeting students in EE and CS. But anyone up for it is welcome.
EE103 was developed by Stephen Boyd and his band of co-conspirators: Ahmed Bou-Rabee, Keegan Go, Jenny Hong, Karanveer Mohan, Jaehyun Park, and David Zeng. They are (or were, in the case of Jaehyun) Stanford undergraduates, majoring in CS or Math. They helped create the course materials, and served as section leaders the first time the course was offered.
EE103 is based on a book that Stephen Boyd and Lieven Vandenberghe (at UCLA) are currently writing. The book is only in draft form now; we will post updated version as they become available.
Matrix methods should not be a spectator sport. Students will use a new language called Julia, developed at MIT, to do computations with matrices and vectors.
EE103 is part of the EE and MS&E core requirements now. But within a year or so, we expect EE103 to satisfy mathematics requirements in various other undergraduate programs. You can probably petition to have it count in your program before it does officially.