MATH169 CM - Representations of High-Dimensional Data

In today’s world, data is exploding at a faster rate than computer architectures can handle. For that reason, mathematical techniques to analyze large-scale objects must be developed. One mathematical method that has gained a lot of recent attention is the use of sparsity. Sparsity captures the idea that high dimensional signals often contain a very small amount of intrinsic information. In this course, we will explore various mathematical notions used in high dimensional signal processing including wavelet theory, Fourier analysis, compressed sensing, optimization problems, and randomized linear algebra. Students will learn the mathematical theory, and perform lab activities working with these techniques.

Prerequisite: MATH 060 CM

Offered: Every other spring

Credit: 1

Course Number: MATH169 CM