Machine Learning for Electrical Engineering

Catalog description

Credits 3. 2 Lecture Hours. 3 Lab Hours. Engineering application-focused introduction to machine learning covering key machine learning concepts, guidance on selecting machine learning models, and application of python-based tools for data preparation, model development, and performance evaluation; practical engineering use-cases for machine learning from electronics, energy, motors, robotics, security, computer systems, and health; machine learning laboratory project including dataset management, ML model development, visualization, and deployment to an IoT platform showcasing ML expertise. Prerequisites: Grade of C or better in ENGR 102 ; grade of C or better in MATH 251 or MATH 253 .