EE103/CME103: Introduction to Matrix Methods

This is the website for EE103/CME103, Autumn quarter 2017–18.

EE103/CME103 will next be taught in Autumn quarter 2018–19 by Professor Brad Osgood.

About EE103

EE103 is a relatively new class, taught for the first time Autumn quarter 2014–15. EE103 covers the basics of vectors and matrices, 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. Matrix methods should not be a spectator sport. In this course, students use a relatively new language called Julia to do computations with vectors and matrices.

The course is suitable for any undergraduate with the prerequisites or equivalent background. Anyone up for it is welcome.

EE103 was developed by Stephen Boyd and his band of (then undergraduate) co-conspirators: Ahmed Bou-Rabee, Keegan Go, Jenny Hong, Karanveer Mohan, Jaehyun Park, and David Zeng.

The class is based on a book by Stephen Boyd and Lieven Vandenberghe (at UCLA), which is available on-line.

EE103 is part of the EE and MS&E core requirements, and certified as a Ways of Thinking course for both formal reasoning (FR) and applied quantitative reasoning (AQR). Additionally, this course is approved for the Computer Science BS Math Elective and also satisfies the Mathematics & Statistics requirement in the School of Engineering.