Graduate Certificate in Artificial Intelligence (Professional)
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Admission Requirements
This certificate program is open to students with a bachelor's degree in any academic area and who are not majoring in Artificial Intelligence. Students must satisfy the prerequisites for each course in the program. The average GPA of all four courses counted in the program must be 3.0 or better. This certificate requires four courses that have not been counted in any other minor or certificate program within the College of Engineering and Computer Science.
Degree Requirements
The certificate has two tracks: a Development track and an Applications track. The Development track is intended for students proficient in programming who will develop new algorithms and mechanisms in artificial intelligence. The Applications track is open to students who have introductory programming skills and are interested in learning how to use the tools and algorithms of artificial intelligence. Students in both tracks are expected to have completed a statistics course.
Development Track (12 credits)
Required courses (6 credits); select two courses from the following
Course Title | Course Number | Credits |
|---|---|---|
Computational Foundations of Artificial Intelligence | CAP 5625 | 3 |
Artificial Intelligence | CAP 6635 | 3 |
Data Mining and Machine Learning | CAP 6673 | 3 |
Elective courses (6 credits); select two courses from the Elective Table
Applications Track (12 credits)
(Not open to graduate students in the Department of Electrical Engineering and Computer Science, except for students in the M.S. with Major in Information Technology and Management (MSITM))
Required courses (6 credits); select two courses from the following
Course Title | Course Number | Credits |
|---|---|---|
Computational Foundations of Artificial Intelligence | CAP 5625 | 3 |
Applied Machine Learning | CAP 6610 | 3 |
Data Mining and Machine Learning | CAP 6673 | 3 |
Elective courses (6 credits); select two courses from the Elective Table
Elective Table
Select two courses from the list below. Additional courses may be used as electives with prior approval of the advisor.
Course Title | Course Number | Credits |
|---|---|---|
Introduction to Neural Networks | CAP 5615 | 3 |
Introduction to Data Science | CAP 5768 | 3 |
Data Mining for Bioinformatics | CAP 6546 | 3 |
Sparse Learning | CAP 6617 | 3 |
Deep Learning | CAP 6619 | 3 |
Natural Language Processing | CAP 6640 | 3 |
Reinforcement Learning | CAP 6629 | 3 |
Artificial Intelligence in Medicine and Healthcare | CAP 6683 | 3 |
Advanced Data Mining and Machine Learning | CAP 6778 | 3 |
Computer Performance Modeling | CEN 6405 | 3 |
Robotic Applications | EEL 5661 | 3 |
Industrial Automation | EIN 5603C | 3 |
Intelligent Transportation Systems | TTE 6272 | 3 |