Artificial Intelligence (BS)
Marymount’s Artificial Intelligence (AI) program recognizes the increasing focus on the use of artificial intelligence techniques in government, business, and society. This major prepares students to be developers and software engineers in the growing and exciting artificial intelligence/machine learning ecosystem.
The AI program provides the following:
- A solid foundation of technology, computer science, cloud computing, and data science concepts that form the basis of AI applications in today’s world;
- Specific skills in developing AI and machine learning applications across a wide range of platforms, in government, in finance, in science and in society to name a few;
- Specific skills in advanced AI uses such as in computer vision and robotics, from manufacturing to self-driving cars;
- A focus on ethics and the concept of trustworthy and explainable AI and the ability to communicate the workings of AI to the end user; and
- Learning new methodologies and technologies as they arise.
Marymount University has worked with community colleges in the region to maximize the credits that can be transferred into the program, including credits for many industry certifications.
The faculty for this program, full time and part time, from government and industry, are experienced and knowledgeable in the specific subjects they teach and, when applicable, use extensive hands-on activities to support learning. Research opportunities are also available.
Students are encouraged to engage in research with full-time faculty in areas such as computer hardware, machine learning, robotics, and natural language processing (NLP).
Internship Prerequisites: All students must take a for-credit internship or experiential learning opportunity in the artificial intelligence field before graduation. A minimum of 90 credits with a minimum cumulative GPA of 2.0 is required to register for the internship.
Minimum Grade Requirements: A minimum grade of C is required in all IT and DATA courses. A minimum grade of C+ is required for IT 489 Capstone Project.
Residency Requirement: Students must complete 21 credits of their artificial intelligence major at Marymount.
Degree Requirements - Artificial Intelligence
This degree requires at least 122 total credits.
Liberal Arts Core Requirements
See the Liberal Arts Core for details.
Major Requirements
To fulfill the requirements of the major, all students in this program will take the following coursework in a sequence determined in collaboration with an advisor. Some courses also satisfy Liberal Arts Core requirements.
Math Requirements
Core Requirements
A minimum grade of C is required in all IT and DATA courses. A minimum grade of C+ is required for IT 489 Capstone Project.
Sample Degree Plan - Artificial Intelligence
Year One - Fall
| EN 101 | Composition I * | 3 |
| IT 112 | Introduction to Computer Systems | 3 |
| MA 181 | Calculus I | 4 |
| | Social Science (SS) core course - PSY 101 recommended * | 3 |
| | Philosophy (PH) core course * | 3 |
EN 101: WR core course
MA 181: MT core course
Year One - Spring
| EN 102 | Composition II * | 3 |
| IT 129 | Python Scripting | 3 |
| IT 150 | Concepts in Artificial Intelligence | 3 |
| MA 218 | Probability and Statistics * | 3 |
| TRS 100 | Theological Inquiry * | 3 |
Year Two - Fall
| IT 229 | Advanced Python Applications | 3 |
| PH 313 | Cyberethics * | 3 |
| HI | History (HI) core course * | 3 |
| | Literature (LT) core course * | 3 |
| | Natural Science (NS) core course with lab * | 4 |
Year Two - Spring
| IT 208 | Computer Networking | 3 |
| IT 212 | Software Architecture and Design | 3 |
| IT 250 | Robotics and Embedded Systems | 3 |
| MA 200 | Calculus II | 3 |
| | Fine and Performing Arts (FPA) core course * | 3 |
Year Three - Fall
Year Three - Spring
| DATA 370 | Machine Learning Technologies | 3 |
| IT 210 | Software Engineering | 3 |
| IT 321 | Cloud Computing | 3 |
| IT 345 | Human Computer Interaction | 3 |
| | One (1) elective | 3 |
Year Four - Fall
| DATA 450 | Advanced Machine Learning | 3 |
| IT 352 | AI: Trust, Bias and Societal Impacts | 3 |
| IT 385 | Managing Big Data | 3 |
| IT 490 | IT Internship | 3 |
| | Depth in Humanities core course | 3 |
Year Four - Spring
| DATA 360 | Natural Language Processing (NLP) | 3 |
| DATA 395 | Data Visualization | 3 |
| IT 489 | Capstone Project | 3 |
| | Social Science (SS) core course * | 3 |
| | One (1) elective | 3 |