Welcome!

Signal Processing is the science that deals with extraction of information from signals of various kinds. This has two distinct aspects -- characterization and categorization. Traditionally, signal characterization has been performed with mathematically-driven transforms, while categorization and classification are achieved using statistical tools.

Machine Learning aims to design algorithms that learn about the state of the world directly from data.

A increasingly popular trend has been to develop and apply machine learning techniques to both aspects of signal processing, often blurring the distinction between the two.

This course discusses the use of machine learning techniques to process signals. We cover a variety of topics, from data driven approaches for characterization of signals such as audio including speech, images and video, and machine learning methods for a variety of speech and image processing problems.

Grading

Weekly quizzes (25%), homeworks (50%), project(25%)

Prerequisites

Mandatory: Linear Algebra, Basic Probability Theory.

Recommended: Signal Processing, Machine Learning

Class Times

Tuesdays and Thursdays, 3:00-4:20pm at Baker Hall BH 136A.

Pat Conrey

Hi team! This is the development site for the course. Almost everything here was copied over from Fall 2019. I just wanted to flesh out the material so that you can get the sense of what we can do.

P.S.: How cool is this little notification! I think we could use these to point out when homeworks go live :)