EE 378B – Statistical signal processing, part B

Traditional statistical signal processing focused on methods to extract information from signals that are indexed by time (eg audio or electromagnetic signals). A growing number of applications and mathematical/algorithmic techniques deals with data or signals that do not fit this framework. Examples include: distance measurements in a sensor network; traffic/delay measurements in the internet; searches at web server; large texts and images; and proximity graphs.

This is a broad area at the intersection of signal processing, statistical learning, and computer science. The focus of this year class will be on methods to model data through matrices, and algorithms to analyze them, as well as on the theory capturing their properties.

Class Times and Locations

• Tue-Thu 12:50PM-2:05PM, Room 60-120