Online Doctor of Engineering in Artificial Intelligence & Machine Learning

We are now accepting applications for the cohort beginning in August 2025. 

Program Description

The online Doctor of Engineering in Artificial Intelligence & Machine Learning is a research-based doctoral program. The program is designed to provide graduates with a solid understanding of the latest AI&ML techniques, as well as hands-on experience in applying these techniques to real-world problems. Graduates of this program are equipped to lead AI&ML projects and teams in a wide range of industries, including healthcare, finance, and manufacturing. Having developed advanced research skills, graduates are also well-prepared for academic research and teaching roles.

Curriculum

The degree requires completion of eight graduate-level courses (listed below) and a minimum of 24 credit hours of Praxis Research (SEAS 8588). During the research phase, the student writes and defends a research praxis on a topic related to AI&ML. The topic is selected by the student and approved by the research advising committee.

Learn More About the Courses

SEAS 6414 Python Application for Data Analytics: Introduction to Python programming tailored for Data Analytics. This course covers Python’s applications in automating data cleaning, feature engineering, outlier detection, implementing machine learning algorithms, conducting text mining, and performing time series analysis. (3 credit hours)

SEAS 8500 Fundamentals of AI-Enabled Systems: Operational decomposition for AI solutions, engineering data for algorithm development, and deployment strategies. Systems perspective in designing AI systems. Full-lifecycle of creating AI-enabled systems. Ethics and biases in AI systems (3 credit hours)

SEAS 8505 Applied Machine Intelligence and Reinforcement Learning: Theory and practice of machine learning leveraging open-source tools, algorithms and techniques. Topics include intelligent model training, support vector machines, deep learning, transformer methods, GANs, and reinforcement learning (3 credit hours)

SEAS 8510 Analytical Methods for Machine Learning: Mathematical tools for building machine learning algorithms: linear algebra, analytical geometry, matrix decompositions, optimization, probability and statistics (3 credit hours)

SEAS 8515 Data Engineering for AI: Developing Python scripts to automate data pipelines, data ingestion, data processing, and data warehousing. Machine learning applications with Python including text mining and time series analysis (3 credit hours)

SEAS 8520 Deep Learning and Natural Language Processing: Fundamentals of deep learning and Natural Language Processing (NLP). Techniques for designing modern deep learning networks using Keras and TensorFlow. NLP topics include sentiment analysis, bag of words, TFIDF, and Large Language Models (3 credit hours)

SEAS 8525 Computer Vision and Generative AI: Explore AI's visual realm. Learn image processing object detection, and models in generative adversarial networks and neural networks. Master tools for creating AI applications in art, design, ethical considerations, and societal impacts of generative AI technology (3 credit hours)

SEAS 8599 Praxis Development for AI & Machine Learning: Overview of research methods. Aims and purpose of the praxis. Development of praxis research strategies, formulation, and defense of a praxis proposal (3 credit hours)

SEAS 8588 Praxis Research for D.Eng. in AI & Machine Learning: Research leading to the degree of Doctor of Engineering in AI and Machine Learning (24 Credit Hours)  

Classroom Phase Schedule

Classroom courses last 10 weeks each and meet on Saturday mornings from 9:00 AM—12:10 PM and afternoons from 1:00—4:10 PM (all times Eastern). All classes meet live online through synchronous distance learning technologies (Zoom). All classes are recorded and available for viewing within two hours of the lecture. This program is taught in a cohort format in which students take all courses in lockstep. Courses cannot be taken out of sequence, live attendance at all class meetings is expected, and students must remain continuously enrolled. Leaves of absence are permitted only in the case of a medical or family emergency, or deployment to active military duty. 
Please see below for the dates of our upcoming cohort.

Semester	Session	#Credit Hours	Tentative Dates
Fall-1 2025	1	6	August  16 — October 18, 2025
Fall-2 2025	2	6	November 1 — January 17, 2025
Spring-1 2026	1	6	January 31 — April 4, 2026
Spring-2 2026	2	6	April 18 — June 27, 2026

No classes on Thanksgiving, Christmas, New Years, and, Memorial Day weekends.

Research Phase Schedule

To proceed to the research phase, students must earn a grade point average of at least 3.2 in the eight classroom courses, and no grade below B-. Students are then registered for a minimum of 24 credit hours of SEAS 8588 Praxis Research: 3 ch in Summer 2025 (Session 2), 9 ch in Fall 2026, 9 ch in Spring 2027, and 3 ch in Summer 2027. Throughout the research phase, students develop the praxis under the guidance of a designated faculty advisor. Faculty research advisors are assigned by the program office and meet individually with students every two weeks.

Sample research areas are listed below:

•    Developing algorithms and methods that can explain how AI systems reach their decisions or predictions, making them more transparent and trustworthy
•    Investigating how reinforcement learning can improve robotic performance and control, particularly in complex environments
•    Examining how to ensure that AI systems are fair and unbiased in their decision-making, particularly in areas such as hiring, lending, and criminal justice
•    Developing more advanced natural language processing models and algorithms that can understand and interpret human language more accurately and effectively
•    Investigating how to apply transfer learning techniques to improve the performance of AI systems in new and different domains, with less data and less training time 

Tuition

Tuition is $1,750 per credit hour for the 2024-2025 year and is billed at the beginning of each semester for the courses registered during that semester. A non-refundable tuition deposit of $995, which is applied to tuition due the first semester, is required when the applicant accepts the offer of admission.

Admissions Process

Review the Admissions Requirements

• Bachelor’s and master’s degrees in engineering, applied science, business, computer science, or a related field from accredited institutions.
• A minimum graduate-level GPA of 3.2
• Capacity for original scholarship.
• TOEFL, IELTS, Duolingo, or PTE scores are required of all applicants who are not citizens of countries where English is the official language.  Check our International Students Page to learn about the SEAS English language requirements and exemption policy. Test scores may not be more than two years old.

Note: GRE and GMAT scores are not required

Please note that our doctoral programs are highly selective; meeting minimum admissions requirements does not guarantee admission.  

Apply for Admission and Submit Supporting Documents

Apply for Admission

• Attach up-to-date Resume 
• Attach Statement of Purpose – In an essay of 250 words or less, state your purpose in undertaking graduate study at The George Washington University. Describe your academic objectives, research interests, and career plans. Discuss your qualifications including collegiate, professional, and community activities, and any other substantial accomplishments not mentioned.
• Send Official Transcripts – Official transcripts are required from all institutions where a degree was earned. Transcripts should be sent electronically to [email protected] or via mail to:
  • Online Engineering Programs
    The George Washington University
    170 Newport Center Drive
    Suite 260
    Newport Beach, CA 92660

Normally, all transcripts must be received before an admission decision is rendered for the Doctor of Engineering program. 

Remain Engaged in the Admissions Process

You will receive emails from us updating you as your application goes through the admissions process.