Schedule of Classes

Orthogonal signal representation; review of Fourier series and Fourier transform; basic probability theory; random processes; power spectral density; Shannon's channel capacity; sampling theorem; baseband signaling; bandpass signaling; complex envelop representation of signals and systems; analog modulations; binary and M-ary digital modulations; phase locked loops, demodulation circuits; matched filter; error performance in digital communications. Cross-listed as ECE 431.

This course covers the theory, design, and engineering applications of machine learning with the emphasis on computational intelligence. Embedded hardware platforms, high-performance libraries, and high-performance architectures are used for implementation. Variants such as Deep Neural Networks and Convolutional Neural Networks are examined. Cross-listed as ECE 465.

Topics of special interest which may vary each time course is offered. Topic stated in current Schedule of Classes. Repeatable to a maximum of 6 semester hours.

Graduate research on a project selected by student and advisor. Repeatable to a maximum of 6 semester hours.