Artificial Intelligence (AI) is a transformative field of computing that is reshaping problem-solving, decision-making, and automation across industries and government. AI technologies are driving advancements in critical areas such as healthcare, finance, national security, autonomous systems, and scientific discovery. From machine learning-powered recommendation systems to deep learning models capable of generating human-like responses, AI is revolutionizing the way we interact with technology and approach complex challenges.
AI now encompasses a diverse set of technologies and methodologies that enable computers and systems to perform tasks traditionally requiring human intelligence. These tasks include problem-solving, reasoning, learning from data, understanding natural language, and recognizing patterns. AI-driven solutions are now fundamental to business innovation, public policy, and scientific progress, making AI expertise essential for professionals across multiple domains.
The Master of Science in Artificial Intelligence provides students with a rigorous foundation in the principles and methodologies of AI, equipping them with the skills to develop, implement, and optimize AI-driven solutions. The program focuses on three core domains within AI: machine learning, planning and decision-making, and deep learning. Machine learning enables AI systems to automatically improve from data, planning and decision-making algorithms support AI-driven systems in navigating complex environments, and deep learning techniques power applications such as image recognition, natural language processing, and generative AI models.
Students in the program will learn to assess AI risks across computing platforms—including embedded systems and cloud computing—to ensure secure and responsible AI applications. They will develop the expertise to train and fine-tune AI models for optimized system performance. Additionally, the program emphasizes the ethical and societal considerations of AI, ensuring graduates are prepared to contribute to the responsible development and deployment of AI technologies.
Admissions
Admission is competitive. Strong candidates will have obtained a BS in an engineering or quantitative discipline. Specific application requirements and deadlines can be found at: https://cec.gmu.edu/application-requirements-and-deadlines.
Policies
Please see AP.6. Graduate Policies.
Degree Requirements
Total credits: 30
Required Coursework
Students must complete 18 credits of required coursework:
| Code | Title | Credits |
|---|---|---|
| AII 600 | Foundations and Practice of Machine Learning for Artificial Intelligence | 3 |
| AII 601 | Planning and Decision Making for Intelligent Agents | 3 |
| AII 602 | Foundations and Practice of Deep Learning for Artificial Intelligence | 3 |
| AII 603 | Engineering Artificial Intelligence Systems and Pipelines | 3 |
| ECE 590/ME 576 | Selected Topics in Engineering (AI: Ethics, Policy, and Society) | 3 |
| GBUS 662 | Management of Information Technology and The Digital Enterprise | 3 |
| Total Credits | 18 | |
Electives
Students must complete 3 credits from each of the following four tracks (12 credits):
- Track 1: AI Policy, Ethics, and Society
- Track 2: Advanced AI
- Track 3: Scalable and Secure AI Infrastructures
- Track 4: Use-inspired AI
AI: Policy, Ethics, and Society
| Code | Title | Credits |
|---|---|---|
| Choose one: | 3 | |
| Law and Ethics of Big Data | ||
| National Security Technology and Policy | ||
| Data Analysis for Global Political Economy | ||
| New Technologies in the Global Economy | ||
| AI Design and Deployment Risks | ||
| Big Data Analytics for Policy and Government | ||
Advanced AI
| Code | Title | Credits |
|---|---|---|
| Choose one: | 3 | |
| Interactive Machine Learning and Artificial Intelligence | ||
| Human-AI Interaction | ||
| Introduction to Natural Language Processing | ||
| Natural Language Processing with Deep Learning | ||
| GenAI and LLM Technologies | ||
| Bayesian Artificial Intelligence | ||
or SYST 664 | Bayesian Artificial Intelligence | |
| Reinforcement Learning | ||
Scalable and Secure AI Infrastructures
| Code | Title | Credits |
|---|---|---|
| Choose one: | 3 | |
| Fundamentals of Computing Platforms | ||
| Cyber Security Fundamentals | ||
| Cloud Computing Security | ||
| IoT and Edge Systems | ||
| AI and Cybersecurity | ||
| Special Topics in Computer Science (AI Safety and Assurance) | ||
| Machine Learning for Embedded Systems | ||
| Machine Learning Security and Privacy | ||
Use-inspired AI
| Code | Title | Credits |
|---|---|---|
| Choose one: | 3 | |
| Interpretable Machine Learning | ||
| Artificial Intelligence Methods for Cybersecurity | ||
| Probabilistic Machine Learning | ||
The plan of study consists of 30 credit hours, 18 of which are required/core coursework, and 12 of which are electives. The electives are organized in four thematic tracks, and students will complete at least 3 credit hours in each track.
At the end of this program, students will be able to:
- Identify and execute Artificial Intelligence opportunities to advance Artificial Intelligence research and applications.
- Translate complex Artificial Intelligence technical details into clear, actionable insights for diverse audiences.
- Demonstrate the ability to rapidly adapt to AI advancements and industry trends.
- Apply the entire Artificial Intelligence Operations pipeline, from model development, to model training, tuning, evaluation, selection, and deployment using cutting-edge libraries and tooling platforms and in embedded systems, on the edge, and in the cloud.
- Demonstrate an in-depth understanding of the foundation and practice of AI algorithms and frameworks.
- Implement safe, secure, and trustworthy Artificial Intelligence solutions and evaluate them against Artificial Intelligence risk frameworks.
- Articulate ethical, policy, and societal implications of Artificial Intelligence algorithms and technologies.