The Master of Science (M.S.) with a major in Artificial Intelligence Systems is a 30-credit hour, non-thesis degree that consists of a set of 6 core courses (18 credit hours), one project course (3 credit hours), and 3 electives options (9 credit hours).
Required core
The 6 core courses are in the following areas:
AI Systems:
EGN 6216
Ethics:
LAW 6930 (Machine Learning and Ethics in Law and Regulation)
Fundamental Machine Learning:
EGN 5216
Deep Learning:
Select one of the following two options:
Sensing and Analysis:
Select one of the following three options:
CAP 5416, EEE 6512, or EEL 5406
Security:
Select one of the following three options:
EEL 5739, EEE 6561 ( Fundamentals of Biometric Identification), or CIS 6930 (Trustworthy Machine Learning)
Project in Artificial Intelligence Systems
Elective CORE
Select 3 courses; selections must include at least 1 course from Group 1 and 1 course from Group 2:
• Group 1: AML-DDM (Advanced Machine Learning and Data-Driven Modeling)
• Group 2: AR-HCC (Autonomy, Robotics, and Human-Centered Computing)
• Group 3: UT (unrestricted technical electives)
This group allows the students to take a technical elective course for greater curriculum flexibility. For example, a student interested in computer vision or robotics may take EEE 5502 or EML 6350 to expand the available course options for core and elective. The technical elective courses in this group must be chosen in coordination with the graduate advisor to ensure prerequisite fulfillment and to optimize for achieving student career goals.
Advanced Machine Learning and Data Driven Modeling
e.g., BME 6938, CAP 6617, EEL 5840, EEL 6814, EEL 6825, EEE 6504, ESI 6355, ESI 6492, STA 6703
Autonomy, Robotics, and Human-Centered Computing
e.g., ABE 6005, CAP 5108, CEN 5726, EML 6351
Engineering Education Departmental Course Offerings
| Code | Title | Credits |
|---|---|---|
| EGN 6913 | Engineering Graduate Research | 0-3 |
| EGN 6933 | Special Topics | 1-3 |
| EGS 6012 | Research Methods in Engineering Education | 3 |
| EGS 6020 | Research Design in Engineering Education | 3 |
| EGS 6050 | Foundations in Engineering Education | 3 |
| EGS 6051 | Instructional Design in Engineering Education | 3 |
| EGS 6054 | Cognition, Learning, and Pedagogy in Engineering Education | 3 |
| EGS 6056 | Learning and Teaching in Engineering | 1 |
| EGS 6085 | Advanced Engineering Educational Technology | 3 |
| EGS 6930 | Engineering Education Seminar | 1 |
| EGS 6940 | Foundations of Research to Practice in Engineering Education | 1 |
| EGS 6949 | Research to Practice Experience in Engineering Education | 1-3 |
| EGS 6971 | Research for Master’s Thesis | 1-12 |
| EGS 7979 | Advanced Research | 1-12 |
| EGS 7980 | Research for Doctoral Dissertation | 1-12 |
Artificial intelligence Systems
SLO 1 Knowledge
To analyze, design, implement, and evaluate an AI systems solution to meet a given set of system requirements.
SLO 2 Skills
To recognize professional responsibilities and make informed decisions when developing AI systems based on legal, ethical, and policy principles.
SLO 3 Professional Behavior
To function effectively as a member of a team engaged to develop an AI systems solution.