George Washington University

How this program compares

George Washington University's Master of Engineering in Artificial Intelligence and Machine Learning lists about $36K total tuition — roughly 16% below the $42.9K AI master's average (54th percentile in our data). It is one of the 54% of programs in our database offered fully or partly online.

Admission Snapshot

Typical admitted student: Applicants must hold a bachelor's degree from a regionally accredited college or university (or equivalent international credential). A statement of purpose describing personal and professional interests, along with letters of recommendation and standardized test scores, are required.[1

About This Program

This online program provides a comprehensive foundation in the data science and computational methods that underpin modern AI-driven technologies. Coursework concentrates on Computer Vision and AI Ethics/Policy. Most students complete it in about 1 year.

It is one of 15 AI-related programs we track in District of Columbia, of which about 60% offer an online option. On price, it comes in cheaper than about 67% of the District of Columbia programs in our data, where typical tuition runs near $83.4K.

Graduates frequently move into roles such as Machine Learning Engineer, with reported salaries around $115,000.

Master core ML algorithms, deep learning techniques, and supervised/unsupervised learning models for solving complex engineering problems.

At roughly $36K total, George Washington University's Master of Engineering in Artificial Intelligence and Machine Learning sits below the $42.9K average among AI master's programs we track — the 54th percentile on price. That puts it in the mid-range on price among comparable programs.

Career Outcomes

Graduates of George Washington University's Master of Engineering in Artificial Intelligence and Machine Learning most often target Machine Learning Engineer roles, where reported compensation runs around $115,000. Its focus on Computer Vision and AI Ethics/Policy maps directly to how employers screen for specialized skills rather than generic degrees. Federal projections for this occupational area point to roughly 36% growth this decade — verify the current figure on the BLS Occupational Outlook Handbook before you rely on it.

• 1. Machine Learning Engineer
• 2. AI Systems Architect
• 3. Data Science Engineer
• 4. AI Research Scientist

What You'll Learn

• Master core ML algorithms, deep learning techniques, and supervised/unsupervised learning models for solving complex engineering problems
• Apply machine intelligence and reinforcement learning to real-world systems and data-driven decision-making
• Develop proficiency in Python and data engineering tools for building AI-enabled systems at scale
• Understand the societal impacts of AI and ML, including ethical considerations in AI deployment

Curriculum Highlights

The 30-credit curriculum covers cloud management, python applications, and autonomous systems, culminating in a comprehensive artificial intelligence capstone project.

Top Employers

Top employers for AI and ML professionals include Google, Microsoft, Amazon, Meta, IBM, and specialized AI firms like OpenAI and DeepMind, as well as government agencies and financial institutions investing heavily in AI infrastructure.

Admissions

Admission to George Washington University's Master of Engineering in Artificial Intelligence and Machine Learning generally expects a bachelor's degree. Plan for the GRE, which this program lists as specific score requirements not specified; standardized test scores required. Deadlines, testing policies and funding change year to year, so confirm the current requirements on the official program page before applying.

Application Materials

• Statement of Purpose: Required
• Letters of Recommendation: Required
• Resume: Required
• Official Transcripts: Required
• Completed Application Form: Required

Frequently Asked Questions