MSAI+ Degree Plan
The MSAI+ degree requires 30 credit hours with 21 hours on AI core curriculum, 6 hours on focus areas, and 3 hours on a focus area related capstone project or internship as shown in Table 1. Students meet graduation criteria with a cumulative GPA of 3.0 or better across all academic courses.
Core Courses (21 Hours)
The core courses reflect the foundation of the AI field. Both the topics of the courses and the content of each course are closely monitored to reflect the latest technology change and development.
| Course Name | Availability |
|---|---|
| Ethics in AI and Responsible AI | CSI 5320 |
| Foundations of Artificial Intelligence | CSI 5328 |
| Introduction to Machine Learning | CSI 5325 |
| Data Mining and Analysis | CSI 5355 |
| Generative AI and Agentic AI | CSI 5375 |
| Cloud Computing | CSI 5357 |
| Advanced Deep Learning | CSI 6326 |
| AI Systems Design | CSI 5377 |
AI+ Focus Areas (6 Hours)
The program is intentionally designed to foster interdisciplinary collaboration across Baylor’s colleges and departments, reflecting AI’s pervasive influence in different domains. Four focus areas will enable students to specialize their AI training within real-world contexts. Students are able to add other focus areas besides the ones listed. The collaboration will provide essential faculty expertise, specialized lab resources, and relevant domain data. The areas are closely linked to the strengths of Computer Science department and Baylor’s other academic units as well as reflecting industrial needs. Interdisciplinary faculty from other Baylor colleges and schools will contribute domain-specific learning through advising on focus area courses and capstone project design. Students will be exposed to domain-specific data, customized AI methodologies, tools used in the focus area, and the latest development and challenges for the field.
CYBERSECURITY
With CS department’s Cybersecurity undergraduate major, investment in several new faculty, and the state-of-the-art Texas Cyber Range infrastructure at the Baylor Innovation Research Center (BRIC), AI for cybersecurity or cybersecurity in AI is a natural focus area for the program. ECS have in-house graduate cybersecurity courses available to students and our faculty have experience in this disciplinary field. Faculty from the departments of Computer Science and Electrical and Computer Engineering collaborate on capstone projects like cybersecurity in networks or cyber physical systems.
| Course Name | Availability |
| Cybersecurity Concepts | CSI 5361 |
| Cybersecurity Analytics | CSI 5367 |
| Cybersecurity for Artificial Intelligence | CSI 5369 |
BIOMEDICAL AND HEALTHCARE INDUSTRY
Numerous faculty at Baylor’s Robbins College of Health and Human Sciences and faculty in the Chemistry and Biochemistry Department are collaborators for this focus area course development and capstone projects. As an example, some potential courses can focus on cancer treatment. Departments involved include Computer Science, Biochemistry and Public Health.
| Course Name | Availability |
| Bioinformatics and System Biology | BINF 5309 |
| Advanced Computational Biology | CSI 5330 |
FINANCIAL ENGINEERING
AI has been transforming financial engineering by enabling the creation of more sophisticated models for risk management, algorithmic trading, and portfolio optimization. Collaborators include Computer Science and the Finance Department of Baylor Business School.
| Course Name | Availability |
| AI in FinTech | CSI 5389 |
| Behavioral Finance | FIN 5338 |
QUANTUM COMPUTING
Faculty in the departments of Computer Science, Physics and Electrical Computer Engineering conduct research in quantum physics. Quantum computing has the potential to exponentially accelerate AI and will revolutionize several critical application areas, such as cryptograph, drug development, optimization, etc.
| Course Name | Availability |
| Quantum Computing | CSI 5370 |
| Quantum AI | CSI 5371 |
Capstone Project (3 Hours)
The three-hour credit semester long capstone project represents the final stage of further refinement of learning with domain-specific application and problem solving. Teams of students (4-5 members) work with faculty and members of industry for hands-on experience with large data sets and the latest AI technology and techniques. Topics will either be carefully chosen by students with input and approval from instructors, or faculty will suggest topics in focus areas.