Machine Learning Concentration

The Electrical and Computer Engineering department offers a concentration in Machine Learning for both the Electrical Engineering and Computer Engineering major degree programs. This concentration reflects recent advances in this field and the departmental concentration will train students in this area which has seen large recent growth in terms of commercial demand for this expertise.

Machine learning is an area of rapidly growing importance. It has applications within several areas of Electrical and Computer Engineering and relies on technology central to Electrical and Computer Engineering. This concentration will equip our students to pursue employment and graduate study in this area. The concentration in Machine Learning is intended for students interested in extra training in topics related to the theory, design and synthesis of intelligent machines with an emphasis on design of machines capable of autonomously learning rules that enable these machines to adapt their behavior from observed measurements.

Faculty contact:

Dr. Andrew Willis, Associate Professor of ECE ( If you are interested to learn more about the Concentration on Machine Learning, please contact Dr. Willis.

Change of Major, Minor or Concentration Form

Machine Learning Concentration Requirements

Students enrolled in the BSEE or the BSCPE program can earn a Concentration in Machine Learning by completing 9 credits of concentration courses as outlined below, usually during the Junior and Senior years. Through careful course selection and scheduling, students can obtain the Concentration in Machine Learning within the required 120 credit hours within the 120 credit hour BSEE or BSCPE curriculum.

Concentration Required Course (3 credit hours)

  • ECGR 4105 – Introduction to Machine Learning (3)

Concentration Elective Courses (6 credit hours)

Select two of the following:

  • ECGR 4090 – Special Topics in Electrical Engineering (1 to 4)*
  • ECGR 4106 – Real-Time Machine Learning (3)
  • ECGR 4115 – Convex Optimization and AI Applications (3)
  • ECGR 4116 – Artificial Intelligence for Biomedical Applications (3)
  • ECGR 4117 – Artificial Intelligence for Robotics and Automation (3)
  • ECGR 4127 – Machine Learning for the Internet of Things (3)

*Topic dependent and requires departmental approval

Admission and progression requirements

Information on admission and progression requirements as well as other program requirements for the concentration can be found in the Undergraduate Catalog by following the corresponding link: