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
Core 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/ME 576 | AI: Ethics, Policy, and Society | 3 |
| GBUS 662 | Management of Information Technology and The Digital Enterprise | 3 |
| Total Credits | 18 | |
Electives
| Code | Title | Credits |
|---|---|---|
| Select four courses from the following: | 12 | |
| Foundations of Applied AI | ||
| AI Application Development | ||
| Interactive Machine Learning and Artificial Intelligence | ||
| Human-AI Interaction | ||
| Natural Language Processing: Theory and Practice | ||
| Cyber Security Fundamentals | ||
| Cloud Computing Security | ||
| Law and Ethics of Big Data 1 | ||
or ECE 575 | AI Design and Deployment Risks | |
or ME 575 | AI Design and Deployment Risks | |
or SYST 575 | AI Design and Deployment Risks | |
| AI and Cybersecurity | ||
| Natural Language Processing with Deep Learning | ||
| Mining Massive Datasets with MapReduce | ||
| Advanced Natural Language Processing | ||
| Computer Vision | ||
| Autonomous Robotics | ||
| Advanced Artificial Intelligence | ||
| Special Topics in Artificial Intelligence and Databases | ||
| Generative Deep Learning | ||
| Advanced Machine Learning | ||
| Cyber Risk Analysis and Advanced Tools | ||
| Introduction to Federated Learning: Fundamentals and Applications | ||
| Artificial Intelligence Methods for Cybersecurity | ||
| Advanced Artificial Intelligence Methods for Cybersecurity | ||
| GenAI and LLM Technologies | ||
| Machine Learning for Embedded Systems | ||
| Hardware Accelerators for Machine Learning | ||
| Machine Learning Security and Privacy | ||
| Advanced GPU Programming and Deep Learning | ||
| Probabilistic Machine Learning | ||
or STAT 646 | Probabilistic Machine Learning | |
| Artificial Intelligence in Health | ||
| Bayesian Artificial Intelligence | ||
or SYST 664 | Bayesian Artificial Intelligence | |
| Simulation and Artificial Intelligence | ||
or SYST 735 | Simulation and Artificial Intelligence | |
| Reinforcement Learning | ||
| Systems Engineering and Artificial Intelligence | ||
or ME 578 | Systems Engineering and Artificial Intelligence | |
| Total Credits | 12 | |
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.
Information Technology, BS/AI: Artificial Intelligence, Accelerated MS
Overview
Highly-qualified undergraduates may be admitted to the combined bachelor's and accelerated master's degree pathway program (accelerated master’s) and obtain the Information Technology, BS and an AI: Artificial Intelligence, MS in an accelerated time-frame after satisfactory completion of a minimum of 138 credits (total number of required credits depends on the requirements of both the undergraduate and graduate programs).
See AP.6.7 Bachelor's/Accelerated Master's Degree for policies related to this program.
Students in an accelerated master’s degree program must fulfill all university requirements for the master's degree. For policies governing all graduate degrees, see AP.6 Graduate Policies.
BAM Pathway Admission Requirements
Applicants to all graduate programs at George Mason University must meet the admission standards and application requirements for graduate study as specified in Graduate Admissions Policies and accelerated master's degree policies.
Students will be considered for admission into the BAM Pathway after completion of a minimum of 60 credits with an overall GPA of at least 3.3.
Students who are accepted into the BAM Pathway will be allowed to register for graduate level courses after successful completion of a minimum of 75 undergraduate credits and course-specific pre-requisites.
Accelerated Master's Admission Requirements
Undergraduate students already admitted to the BAM Pathway will be admitted to the intended master's program, if they have met the following criteria, that will be verified:
- Submission of BAM Transition Form by stated deadline.
- Sufficient minimum 3.0 cumulative GPA for conferred undergraduate degree (which does not include any earned reserve graduate credits).
- Completion of approved advanced standing courses and any reserve graduate courses that have met the minimum grade requirement.
- Successful completion of required minimum of 120 credits needed for undergraduate degree conferral (after exclusion any satisfactory reserve graduate credits earned).
- Successfully meeting Mason’s requirements for undergraduate degree conferral (graduation) and timely submitting the application for graduation.
Accelerated Pathway Requirements
To maintain the integrity and quality of both the undergraduate and graduate degree programs, undergraduate students interested in taking graduate courses must choose from the following:
Advanced Standing courses: Students must complete at least 3 credits from the following list of graduate-level courses, while in undergraduate status, up to a maximum of 12.
| Code | Title | Credits |
|---|---|---|
| AII 600 | Foundations and Practice of Machine Learning for Artificial Intelligence (satisfies IT 416) | 3 |
| AIT 536 | Foundations of Applied AI (satisfies IT 371) | 3 |
| AIT 618 | Human-AI Interaction (satisfies SYST 469) | 3 |
| AIT 670 | Cloud Computing Security (satisfies IT 481) | 3 |
Reserve Credit Courses: Students may complete up to 6 credits, while in undergraduate status, of graduate-level coursework that will only count toward the graduate degree program. Reserve graduate credit must be selected from courses that fulfill AI: Artificial Intelligence, MS degree requirements.
For more detailed information on coursework and timeline requirements, see AP.6.7 Bachelor's/Accelerated Master's Degree and AP.1 Graduate Course Enrollment by Undergraduates.
Statistics, BS/AI: Artificial Intelligence, Accelerated MS
Overview
Highly qualified undergraduates may be admitted to the combined bachelor's and accelerated master's degree pathway program (accelerated master’s) and obtain the Statistics, BS and an AI: Artificial Intelligence, MS in an accelerated time-frame after satisfactory completion of a minimum of 138 credits (total number of required credits depends on the requirements of both the undergraduate and graduate programs).
See AP.6.7 Bachelor's/Accelerated Master's Degree for policies related to this program.
Students in an accelerated master’s degree program must fulfill all university requirements for the master's degree. For policies governing all graduate degrees, see AP.6 Graduate Policies.
BAM Pathway Admission Requirements
Applicants to all graduate programs at George Mason University must meet the admission standards and application requirements for graduate study as specified in Graduate Admissions Policies and accelerated master's degree policies.
Statistics, BS students will be considered for admission into the BAM Pathway after completion of a minimum of 60 credits, with an overall GPA of 3.0.
Students who are accepted into the BAM Pathway will be allowed to register for graduate level courses after successful completion of a minimum of 75 undergraduate credits and any pathway-specific course pre-requisites.
Accelerated Master's Admission Requirements
Undergraduate students already admitted to the BAM pathway will be admitted to the intended master's program if they have met the following criteria, that will be verified:
- Submission of BAM Transition Form by stated deadline.
- Sufficient minimum 3.0 cumulative GPA for conferred undergraduate degree (which does not include any earned reserve graduate credits).
- Completion of approved advanced standing courses and any reserve graduate courses that have met the minimum grade requirement.
- Successful completion of required minimum of 120 credits needed for undergraduate degree conferral (after exclusion of any satisfactory reserve graduate credits earned).
- Successfully meeting George Mason’s requirements for undergraduate degree conferral (graduation) and completing the application for graduation.
Accelerated Pathway Requirements
To maintain the integrity and quality of both the undergraduate and graduate degree programs, undergraduate students interested in taking graduate courses must choose from the following:
Advance Standing courses: Students must complete at least 3 credits from the following list of graduate-level courses, while in undergraduate status, up to a maximum of 12.
Required course:
| Code | Title | Credits |
|---|---|---|
| STAT 646 | Probabilistic Machine Learning | 3 |
Select the remaining from the following courses:
| 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 |
All graduate course prerequisites must be completed prior to enrollment.
Reserve Graduate Credit: Students may complete up to 6 credits, while in undergraduate status, of graduate-level coursework that will only count toward the graduate degree program. Reserve Graduate Credit must be selected from the curated list of courses above.
For more detailed information on coursework and timeline requirements, see AP.6.7 Bachelor's/Accelerated Master's Degrees and AP.1 Graduate Course Enrollment by Undergraduates.
Degree Conferral
Students must apply the semester before they expect to complete the BS requirements to have the BS degree conferred. In addition, at the beginning of the student's final undergraduate semester, students must complete a Bachelor's/Accelerated Master's Transition form that is submitted to the Office of the University Registrar and Graduate Recruitment and Enrollment Services. At the completion of MS requirements, a master's degree is conferred.
Systems and Industrial Engineering, BS/AI: Artificial Intelligence, Accelerated MS
Overview
Highly-qualified undergraduates may be admitted to the combined bachelor's and accelerated master's degree pathway program (accelerated master's) and obtain a Systems and Industrial Engineering, BS and an AI: Artificial Intelligence, MS in an accelerated time-frame after satisfactory completion of a minimum of 141 credits (total number of required credits depends on the requirements of both the undergraduate and graduate programs).
See AP.6.7 Bachelor's/Accelerated Master's Degrees for policies related to this program.
Students in an accelerated master's degree program must fulfill all university requirements for the master's degree. For policies governing all graduate degrees, see AP.6 Graduate Policies.
BAM Pathway Admission Requirements
Applicants to all graduate programs at George Mason University must meet the admission standards and application requirements for graduate study as specified in Graduate Admissions Policies and accelerated master's degree policies.
Students will be considered for admission into the BAM Pathway after completion of a minimum of 60 credits with an overall GPA of at least 3.0.
Students who are accepted into the BAM Pathway will be allowed to register for graduate level courses after successful completion of a minimum of 75 undergraduate credits and course-specific pre-requisites.
Accelerated Master's Admission Requirements
Undergraduate students already admitted to the BAM Pathway will be admitted to the intended master's program, if they have met the following criteria, that will be verified:
- Submission of BAM Transition Form by stated deadline.
- Sufficient minimum 3.0 cumulative GPA for conferred undergraduate degree (which does not include any earned reserve graduate credits).
- Completion of approved advanced standing courses and any reserve graduate courses that have met the minimum grade requirement.
- Successful completion of required minimum of 120 credits needed for undergraduate degree conferral (after exclusion any satisfactory reserve graduate credits earned).
- Successfully meeting Mason’s requirements for undergraduate degree conferral (graduation) and timely submitting the application for graduation.
Accelerated Pathway Requirements
To maintain the integrity and quality of both the undergraduate and graduate degree programs, undergraduate students interested in taking graduate courses must choose from the following:
Advanced Standing courses: Students must complete at least 3 credits from the following list of graduate-level courses, while in undergraduate status, up to a maximum of 12.
These courses may be chosen from the list of graduate courses in the following table. For Systems and Industrial Engineering, BS students, these graduate courses replace the corresponding undergraduate courses listed in the table. The undergraduate version of these courses may not be applied toward the AI: Artificial Intelligence, MS
| Undergraduate | Graduate | |
|---|---|---|
| SYST 478 | SYST 578 | Credit may not be received for both courses |
| SYST 479 | SYST 575 | Credit may not be received for both courses |
| OR 410 | OR 610 | Credit may not be received for both courses |
| OR 464 | SYST 664/OR 664 | Credit may not be received for both courses |
Students must pay attention to the prerequisites required for a course, and the master's degree concentration that the course may satisfy.
Reserve Credit Courses: Students may complete up to 6 credits, while in undergraduate status, of graduate-level coursework that will only count toward the graduate degree program. Reserve graduate credit must be selected from courses that fulfill AI: Artificial Intelligence, MS degree requirements.
For more detailed information on coursework and timeline requirements, see AP.6.7
Bachelor's/Accelerated Master's Degree and AP.1 Graduate Course Enrollment by Undergraduates.
Degree Conferral
Students must apply the semester before they expect to complete the BS requirements to have the BS degree conferred. In addition, at the beginning of the student's final undergraduate semester, students must complete a Bachelor's/Accelerated Master's Transition form. At the completion of MS requirements, a master's degree is conferred.