Please see the attached syllabus for more information.

This course begins with modern formulations of classical signal processing concepts, including signal representations, filtering, and sampling and quantization. Then, recent extensions and generalizations are presented, including sparse approximation and signal recovery, optimization techniques for nonlinear processing of signals, and connections to estimation theory and modeling. Theoretical topics will be connected to applications in communications, imaging, biomedical signal analysis, astronomy, geology, remote sensing, crystallography, big data, and other areas. As time permits, additional topics such as signal processing on graphs and matrices may be introduced. Students will be expected to read scholarly papers from the published literature and complete a course project. A background in probability may be helpful but is not required.