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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; one of CS 361, STAT 361, MATH 461, MATH 463 or STAT 400.