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CS 545 - Machine Learning for Signals

Fall 2021

Official Description

Fundamentals of machine learning and signal processing as they pertain to the development of machines that can understand complex real-world signals, such as speech, images, movies, music, biological and mechanical readings, etc. Hands-on examples of how to decompose, analyze, classify, detect and consolidate signals, and examine various commonplace operations such as finding faces from camera feeds, organizing personal music collections, designing speech dialog systems and understanding movie content. Course Information: 4 graduate hours. No professional credit. Prerequisite: MATH 415; CS 361 or MATH 461 or STAT 400.

Related Faculty

Machine Learning for SignalsMLS75562LEC41230 - 1345 T R  1404 Siebel Center for Comp Sci Paris Smaragdis