Prerequisites:

Mandatory:  Linear Algebra. Basic Probability Theory.

Recommended:  Signal Processing. Machine Learning.

Please sign up to the MLSP-fall-2011 google group to participate in dicussions and receive notices.

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.

Students from the previous iterations (2009 2010) worked on a number of excellent projects, several of which were submitted to conferences. This year we hope to continue this tradition.

A list of potential projects for the fall 2011 edition of the course may be found here.

The planned course schedule will appear here shortly.

There will be several guest lectures. These will be announced as dates are finalized.

Grading will be based on performance in course assignments and a final project.