MATH 4303 - Fourier Analysis for Data Science

Credit(s): 3
Lecture Hour(s): 3
Lecture Hours Schedule Type: Lecture, Blended, Online
Lab Hour(s): 2

The course provides an introduction to techniques of Fourier analysis for data science. Topics include the Fourier Transform, Discrete Fourier Transform and the Fast Fourier Transform both in one variate and multivariate settings. Furthermore, topics such as Convolution, z-Transform and Frequency Filtering are discussed. The focus is given to numerical aspects with data compression and data filtering in mind.

Prerequisite(s): MATH 2200 and MATH 2303, each with a grade of C- or higher.
General Science Option