Artificial Intelligence
The Graduate Certificate in Artificial Intelligence provides a focused, 12-credit introduction to the theory and practice of AI. Students gain hands-on experience building and deploying AI models, analyzing diverse data types, and solving real-world problems using modern tools and techniques. Designed for professionals and students with quantitative backgrounds, the program offers a flexible pathway to develop in-demand skills and can be applied toward NJIT’s M.S. in Artificial Intelligence.
Prerequisites
Applicants should have a bachelor’s degree from an accredited institution in a STEM discipline or have relevant professional experience in computing. Further information can be found here.
Related MS Programs
Students who achieve a GPA of at least 3.0 are assured of admission into MS programs offered by the Ying Wu College of Computing. Courses within this certificate program may fulfill the degree requirements for the MS in AI, MS in DS, or MS in CS program. Current students may also reach out to YWCC advisors for additional information.
Degree Requirements
The graduate certificate in Artificial Intelligence (AI) can be completed by taking four courses (12 credits). The requirements must be satisfied as indicated in the following Course List.
| Code | Title | Credits |
|---|---|---|
| Core Courses | ||
| CS 670 | Artificial Intelligence | 3 |
| DS 675 | Machine Learning | 3 |
| Electives | ||
| Select two from the following: | ||
| CS 669 | Reinforcement Learning | 3 |
| DS 637 | Python and Mathematics for Machine Learning * | 3 |
| DS 677 | Deep Learning | 3 |
| DS 680 | Natural Language Processing | 3 |
| DS 681 | Deep Learning for Computer Vision | 3 |
| DS 683 | Graph Neural Networks | 3 |
| DS 685 | Artificial Intelligence for Robotics | 3 |
| DS 688 | Advanced Federated Machine Learning | 3 |
| DS 698 | Special Emerging Topics | 3 |
| DS 732 | Theoretical Foundation of Machine Learning | 3 |
| or CS 732 | Advanced Machine Learning | |
| DS 733 | Deep Unsupervised Learning | 3 |
| DS 786 | Selected Topics in Data Science | 3 |
| DS 789 | Trustworthy Artificial Intelligence | 3 |
| Total Credits | 12 | |
* DS 637 is recommended as an introductory course, offering a review of mathematics for machine learning to students with a limited background in mathematics or programming.
