AMATH 391 - Data Analysis with Fourier and Wavelet Methods

Fourier and wavelet methods are fundamental tools in data analysis ranging from time-series data to image and audio compression. These methods are increasingly finding application in areas such as machine learning and quantum computing alongside traditional application areas such as signal processing for images and audio. This course covers the mathematical development of Fourier and wavelet analysis, with an emphasis on both theory and practical application. Students will learn the necessary theory to apply Fourier and wavelet methods to the analysis of a variety of practical problems with hands on coding experience through a number of assignments.