Artificial Intelligence, Minor
Program Overview
The Department of Computer and Data Sciences offers a minor in artificial intelligence. This minor provides depth and breadth in artificial intelligence to supplement the student's existing major. The minor assumes prior coursework in computer science and mathematics.
Learning Outcomes
- Students completing the program will have the ability to apply theory, techniques, and tools of artificial intelligence to solve novel problems.
- Students completing the program will be able to explain how some artificial intelligence tools work and what assumptions are implicit in the tools.
- Students completing the program will practice applying artificial intelligence techniques and theory in a variety of contexts.
- Students completing this program will recognize their professional responsibilities and have the tools to make informed judgements about the societal benefits and risks of applications they are building.
Undergraduate Policies
For undergraduate policies and procedures, please review the Undergraduate Academics section of the General Bulletin.
Program Requirements
The minor consists of six courses. No more than four courses can be double counted with the student's major requirements.
| Code | Title | Credit Hours |
|---|---|---|
| Required Courses: | ||
| CSDS 330 | Introduction to Artificial Intelligence | 3 |
| CSDS 340 | Introduction to Machine Learning | 3 |
| Societal Impact Elective | 3 | |
| Choose one of the following: | ||
| Current Issues in Artificial Intelligence, For Better or Worse | ||
| Responsible AI: Cultivating a Just and Sustainable Socio-technical Future through Data Citizenship | ||
| The Politics of Artificial Intelligence | ||
| Ethics of Artificial Intelligence and Emerging Technology | ||
| Technical Electives | 9 | |
| Choose three of the following: | ||
| Artificial Intelligence for All Disciplines | ||
| Numerical Algorithms for Machine Learning | ||
| Data Mining for Big Data | ||
| Computational Perception | ||
| Designing High Performant Systems for AI | ||
| Mobile Robotics | ||
| Machine Learning | ||
| Machine Learning on Graphs | ||
| Causality and Machine Learning | ||
| AI in Medical Imaging | ||
| Computer Vision | ||
| Large Language Models | ||
| Robotics I | ||
| Probabilistic Models in AI | ||
| Sequential Decision Making | ||
| Foundations of Statistical Natural Language Processing | ||
| Algorithmic Robotics | ||
| Introduction to Modern Robotics | ||
| Math in Machine Learning | ||
| Mathematics of Data Mining and Pattern Recognition | ||
| Total Credit Hours | 18 | |