Online Master’s in Artificial Intelligence & Machine Learning

The Online Master’s in Artificial Intelligence & Machine Learning is offered by The George Washington University’s School of Engineering and Applied Science — whose online graduate engineering programs are ranked #5 of 111 by U.S. News & World Report (2026), the highest ranking of any private university in the category. The 30-credit M.Eng. program covers applied machine learning, quantitative foundations in AI, natural language processing with deep learning, computer vision, autonomous systems and robotics, Python applications for data analytics, cloud and big data management, and AI security, privacy, and ethics, culminating in an AI capstone project. Tuition is $1,250 per credit hour ($37,500 total), with eBooks and software included. Classes utilize the “Live Online” modality and meet live one evening per week (6:30–9:30 PM ET, Monday–Thursday) with recordings available if unable to participate in the live class. Students can complete the program in as little as one year and choose from five start terms per year. 

Master’s Degree in Artificial Intelligence

The Master’s Degree in Artificial Intelligence is designed for professionals seeking to advance their expertise in the data science, algorithms, and computational methods that underpin artificial intelligence and machine learning systems while continuing their careers. Offered in a flexible online format, the program delivers rigorous, graduate-level instruction in core AI and machine learning concepts, with an emphasis on intelligent systems, advanced algorithms, and real-world engineering applications. Guided by expert faculty, graduates are prepared to develop, analyze, and deploy AI-driven technologies and pursue advanced roles across technology, finance, healthcare, transportation, or further academic study.

Download the Program Flyer (PDF)

Program Overview

Through graduate-level coursework, students engage in an in-depth artificial intelligence and machine learning curriculum that emphasizes mathematical foundations, advanced algorithms, and applied problem-solving from an engineering perspective. The program integrates theoretical principles with practical application, enabling students to design intelligent systems, engineer and deploy machine-learning–driven solutions, and apply neural networks and other AI techniques across diverse domains.

Taught by expert faculty, the program prepares graduates to advance into specialized professional roles and contribute to the engineering and development of AI-driven technologies while considering ethical implications and societal impact. The degree also provides a strong academic foundation for students interested in doctoral study or advanced research in artificial intelligence, machine learning, or related fields.

What the M.Eng. in AI & Machine Learning Costs at GW

How Much Does This Degree Cost?

Tuition for the online Master’s Degree in Artificial Intelligence is $1,250 per credit hour for the 2026-2027 academic year. With 30 total credit hours, the estimated total tuition cost is $37,500 which is billed per semester based on enrolled courses.

A $495 non-refundable tuition deposit is required upon admission and is applied toward first-semester tuition. The program includes no additional fees, and required eBooks and software are provided at no extra cost. Student veterans, military personnel, and eligible dependents are encouraged to inquire about available military education benefits.

How Long Does the Program Take, and How Is It Delivered?

The online Master’s in Artificial Intelligence can be completed in as little as one year for full-time students, with part-time options available. Courses are delivered fully remote and combine live, scheduled instruction with asynchronous coursework.

Quick facts:

• Format: 100% online
• Class schedule: One evening per week (Monday-Thursday), 6:30-9:30 p.m. ET
• Session length: Nine-week sessions
• Enrollment structure: Students enroll in four nine-week sessions, with an optional fifth summer session
• Start options: Up to five start terms per year

Curriculum Highlights

The curriculum consists of 10 courses (30 total credit hours) designed to build advanced expertise in artificial intelligence and machine learning across core areas of the field. Courses combine synchronous and asynchronous learning, providing live instruction alongside flexible coursework.

Learn more about the courses 

ECE 6210 Machine Intelligence: Machine learning theory; classification and linear models; perceptron model; artificial neural networks; training, inference, software, programming; vector matrix multiplications; design vectors of AI system and performance; AI applications. Prerequisites: Undergraduate-level knowledge in electrical and/or computer engineering or computer science. (3 credit hours)

SEAS 6413 Cloud and Big Data Management: Topics related to big data and cloud computing, including data centers, virtualization, hardware and software architecture, as well as system-level issues on performance, energy efficiency, reliability, scalability and security. (3 credit hours)

SEAS 6414 Python Applications in Data Analytics: Introduction to programming with Python with applications in Data Analytics including automating data cleaning, machine learning, text mining, time series analysis, anomaly detection, DoS attack detection, and spam detection. (3 credit hours)

SEAS 6500 Foundations of AI: Fundamental concepts of AI modeling. Use of Engineering data for algorithm development. Introduction to ethics and biases in AI systems. (3 credit hours)

SEAS 6505 Quantitative Methods for AI: Essential math concepts for AI. Probability & statistics fundamentals. Linear algebra principles. Optimization techniques. Algorithm development foundations. Analytical skills for AI. Technical prowess in AI. AI algorithm analysis. (3 credit hours)

SEAS 6510 Natural Language Processing with Deep Learning: Deep Learning for NLP. Recurrent Neural Networks (RNNs). Long Short-Term Memory (LSTM). Transformers in NLP. Text classification, sentiment analysis, and prompt engineering. Large Language Model. Attention mechanisms. Word embeddings & representation. (3 credit hours)

SEAS 6515 Introduction to Computer Vision: Fundamentals of computer vision. Image processing techniques. Feature extraction methods. Object detection and recognition. Deep learning in computer vision. Convolutional Neural Networks (CNNs). Practical computer vision applications. (3 credit hours)

SEAS 6520 Autonomous Systems & Robotics: Delves into the cutting-edge field of intelligent automation, exploring the design, development, and deployment of autonomous systems. Students engage in advanced studies on robotic systems, artificial intelligence algorithms, and sensor integration. (3 credit hours)

SEAS 6525 Secure & Trustworthy AI: This course seeks to provide students with a thorough understanding of artificial intelligence (AI), Generative AI (GenAI) and Machine Learning (ML), focusing specifically on security, governance, and how to protect sensitive data using AI models. The course is based on a comprehensive and up-to-date handbook. The course is designed to provide AI practitioners, IT professionals, data scientists, security experts, policymakers, and students with the necessary knowledge and tools to securely and responsibly create, implement, and operate AI and ML systems. (3 credit hours)

SEAS 8550 Artificial Intelligence: Law & Ethics: Survey of important law and ethical developments affecting AI in various areas from copyright and other intellectual property, contracts, torts and other laws.   Attention given to both US, EU and other international law recent developments.   Analysis of the various ethical issues and standards that arise from AI and large language models, including intrinsic bias and the significance of training model selection.  (3 credit hours)

Academic Calendar

Session	Dates	Application Deadlines
Summer 2026	week of 6/1/26 – week of 6/29/26	5/16/2026
Fall-1 2026	8/18/26 –  10/15/26	8/10/2026
Fall-2 2026	10/20/26 – 12/17/26	10/5/2026
Spring-1 2027	week of 1/11/27 – week of 3/16/27	12/7/2026
Spring-2 2027	week of 3/22/27 – week of 5/10/27	3/1/2027

The course order is determined by academic advisors based on student progress. Course details are provided through Blackboard approximately one month before classes begin.

What Can You Do With a Master’s in AI & Machine Learning?

Graduates of the online Master’s Degree in Artificial Intelligence are prepared for advanced professional roles focused on developing, deploying, and evaluating AI- and machine-learning–driven systems across technical and organizational environments. The degree supports career advancement for professionals seeking deeper specialization, increased responsibility, and opportunities to contribute to the design and application of intelligent technologies.

SALARY  According to the U.S. Bureau of Labor Statistics (May 2024), the median annual salary for Computer & Information Research Scientists is $140,910, with employment projected to grow 20% from 2024 to 2034 — much faster than the average for all occupations. Data Scientists earn a median of $112,590 with 34% projected growth over the same period, making it one of the fastest-growing occupations in the U.S. economy.

Source: U.S. Bureau of Labor Statistics, Occupational Outlook Handbook — Computer & Information Research Scientists (15-1221) and Data Scientists (15-2051).

Sectors to surface as employer markets for AI/ML graduates:

• Federal government, defense, and intelligence community
• Federal contractors and federally funded research and development centers (FFRDCs)
• Technology and AI research labs
• Healthcare AI and biotech
• Financial services and fintech
• AI safety, policy, and trustworthy AI research

Admissions Requirements

Who Should Apply

The online Master’s in Artificial Intelligence is designed for applicants with a strong academic background in engineering or related technical disciplines who are prepared for advanced, graduate-level study.

Applicants should meet the following requirements:

• A bachelor’s degree in engineering, computer science, information technology, mathematics, physics, or a closely related field from an accredited institution
• A minimum GPA of 3.0 on a 4.0 scale
• A grade of C or higher in at least one college-level calculus course and one college-level statistics course. (Applicants who do not fully meet the calculus or statistics requirement but are otherwise qualified may be conditionally admitted and required to complete an additional 3-credit course during their first semester.)
• Applicants outside the United States should review international admissions requirements for additional criteria
• Applicants may also apply to a six-course graduate certificate in Artificial Intelligence Engineering; upon successful completion of the certificate and reapplication, all certificate courses may be applied toward the Master’s degree*

*Subject to program policies and academic requirements.

Application Materials

There is no application fee for GW’s online engineering programs.

A complete application includes:

• Resume or CV
• Official transcripts from all institutions where a degree was earned
• Two professional letters of recommendation
• GRE scores (optional), which may be submitted if available to strengthen the application

After You Apply

Applicants are expected to remain engaged throughout the admissions process. You will receive email updates as your application is reviewed, and timely responses to requests for materials are required to keep your application moving forward.

Frequently Asked Questions

How does this artificial intelligence program differ from a data science or computer science master’s degree?

This Master’s Degree in Artificial Intelligence focuses on the engineering and deployment of intelligent systems, with emphasis on machine learning, advanced algorithms, and AI-driven technologies. Unlike data science programs that primarily emphasize statistical analysis and data interpretation, or computer science programs with broader theoretical scope, this program centers on building, evaluating, and applying AI and machine learning models in real-world, engineering-focused contexts, while also addressing ethical and societal considerations often explored in a master’s degree in machine learning or related graduate programs.

Do I need an engineering degree to apply?

Applicants should hold a bachelor’s degree in engineering, computer science, or a closely related technical field. Candidates with other quantitative or technical backgrounds may be considered on a case-by-case basis, depending on academic preparation and professional experience.

Is the GRE required?
 

No. The GRE is not required for admission to the online Master’s in Artificial Intelligence.

Can I work full time while enrolled?

Yes. The program is designed for working professionals. Courses are delivered online with one scheduled evening class per week, allowing students to balance coursework with full-time employment.

Are international applicants eligible?

Yes. International applicants are welcome to apply. Additional documentation, including proof of English language proficiency, may be required depending on prior education and background.

Is GW’s online M.Eng. in AI & Machine Learning STEM-designated?

Yes. The online Master of Engineering in AI & Machine Learning is STEM-designated. GW Engineering also co-leads the NSF Institute for Trustworthy AI in Law & Society (TRAILS), and AI/ML research at GW is supported by federal funding from NSF, NIH, DARPA, IARPA, and DOE.

Why choose GW Engineering’s online Master’s in AI & Machine Learning?

GW Engineering is ranked #5 of 111 in U.S. News’ 2026 Best Online Master’s in Engineering Programs — the highest-ranked private university in the category. The program is STEM-designated, NSF TRAILS-affiliated, and built around live evening classes (6:30–9:30 PM ET, Monday–Thursday) with asynchronous coursework — a structure designed for working professionals who want real-time faculty access and the ability to complete the degree in as little as one year. GW’s Washington, D.C. location places the program at the center of the federal AI ecosystem, near federal civilian agencies, defense and intelligence agencies, federally funded research labs, and major contractors. Total tuition is $37,500, with eBooks and software included; no GRE and no application fee are required.

What employers hire GW Engineering AI & Machine Learning graduates?

GW Engineering graduates pursue AI and machine learning careers across the federal AI ecosystem (civilian agencies, defense and intelligence agencies, federally funded research labs, and federal contractors), as well as in technology, financial services, healthcare and biotech, and AI research labs. 

Can the M.Eng. in AI & Machine Learning be completed in one year?

Yes. Full-time students can complete all 30 credit hours in as little as one year. GW offers five start terms per year, allowing accelerated pacing for students who can take multiple courses per term. Part-time students typically complete the program in 18–30 months while working full time. Classes meet live one evening per week (Monday–Thursday, 6:30–9:30 PM ET) with additional asynchronous coursework outside scheduled class meetings.

Explore Related Online M.Eng. Programs

• Online Master’s in Cybersecurity — ideal complement for AI/ML graduates interested in AI security, model robustness, and adversarial ML
• Online Master’s in Cloud Computing Management — covers the infrastructure side of large-scale AI/ML deployment
• Online Master’s in Engineering Management — for AI/ML professionals moving into technical leadership and director-track roles
• Online D.Eng. in Artificial Intelligence & Machine Learning — doctoral pathway for graduates seeking advanced research, leadership, or academic roles

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