Artificial Intelligence Minor
This minor teaches the technical aspects of Artificial Intelligence, including machine learning, deep learning, computer vision, natural language processing, responsible and fair AI, and ethics of AI.
Related Programs
Curriculum
| Code | Title | Hours |
|---|---|---|
| COMP 163 | Discrete Structures | 3 |
| COMP 170 | Introduction to Object-Oriented Programming | 3 |
| COMP 271 | Data Structures I | 3 |
| COMP 317 | Social, Legal, and Ethical Issues in Computing | 3 |
| COMP 329 | Natural Language Processing | 3 |
| COMP 378 | Artificial Intelligence | 3 |
| COMP 379 | Machine Learning | 3 |
| COMP 387 | Deep Learning | 3 |
| MATH 131 | Applied Calculus I | 3-4 |
| or MATH 161 | Calculus I | |
| Total Hours | 27 | |
Note: if this minor is declared by a CSEC-BS student (for example), this will be only 4 extra courses or 12 credit hours.
Suggested Sequence of Courses
The below sequence of courses is meant to be used as a suggested path for completing coursework. An individual student’s completion of requirements depends on course offerings in a given term as well as the start term for a major or graduate study. Students should consult their advisor for assistance with course selection.
| Semester I | Hours | |
|---|---|---|
| COMP 163 | Discrete Structures | 3 |
| COMP 170 | Introduction to Object-Oriented Programming | 3 |
| MATH 131 or MATH 161 |
Applied Calculus I or Calculus I |
3 |
| Hours | 9 | |
| Semester II | ||
| COMP 271 | Data Structures I | 3 |
| COMP 317 | Social, Legal, and Ethical Issues in Computing | 3 |
| Hours | 6 | |
| Semester III | ||
| COMP 378 | Artificial Intelligence | 3 |
| COMP 379 | Machine Learning | 3 |
| Hours | 6 | |
| Semester IV | ||
| COMP 329 | Natural Language Processing | 3 |
| COMP 387 | Deep Learning | 3 |
| Hours | 6 | |
| Total Hours | 27 | |
Undergraduate Policies and Procedures
Please see Undergraduate Policies and Procedures for academic policies that supersede those of academic units within the University.
Learning Outcomes
- Students will be able to articulate and apply fundamental AI concepts, including supervised learning, unsupervised learning, reinforcement learning, and basic neural networks, to solve a range of practical problems.
- Students will be able to select and apply AI techniques to solve specific problems within a chosen domain, demonstrating an understanding of the practical applications of AI.
- Students will demonstrate the ability to critically evaluate the ethical implications of AI systems, including bias, fairness, accountability, and transparency.