EENG 5433G - Machine Learning and Adaptive Control (3 Credit Hours)

Lecture Hours: 3 Lab Hours: 0
Machine Learning is one of the most widely applied technical fields in the academia and industry. The class covers fundamental algorithms in machine learning including linear regression, classification, neural networks, support vector machines, clustering, and introduction to reinforcement learning. This course also covers adaptive control algorithms: including direct and indirect adaptive control. Electrical Engineering applications are demonstrated in image processing, autonomous driving, and robotics. Graduate students will be required to complete additional assignments and/or a culminating research project commensurate with graduate level work that is not required of undergraduate students.

Prerequisite(s): A minimum grade of “C” in EENG 3421 and prior or concurrent in EENG 5431, or permission of instructor.
Cross Listing(s): EENG 5433.
Restriction(s): NO Undergraduate Level Students