Engineering Artificial Intelligence

Catalog description

This course introduces theory and engineering applications of machine learning, including neural networks and deep learning, fuzzy logic, genetic algorithms and statistical methods for supervised, unsupervised and semi-supervised learning. Students will experience programming in AI language tools. Potential areas for further exploration include expert systems, ethical AI, power systems, robotics, natural language processing and computer vision. 3 Lecture hours and 2 Lab hours. Prerequisite: EE 200 or CS 140, Grade of C- or higher. Offered: Spring.