18-797/11-755 Machine Learning for Signal Processing
Location: Pittsburgh
Units: 12
Semester Offered: Fall
Course description
Signal Processing is the science that deals with the 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.
An 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 the characterization of signals such as audio including speech, images, and video to machine learning methods for a variety of speech and image processing problems.
Check the original course description for the most recent information.
Prerequisites
- Linear Algebra
- Basic Probability Theory
- Signal Processing
- Machine Learning
Course format
Online