Online Master of Engineering in Artificial Intelligence and Machine Learning

We are now accepting online AI and Machine Learning master's degree program applications for our fall and spring semester start dates. For more details on Online MS application deadlines and start dates, refer to the academic calendar.

Program Description

The online Master of Engineering in Artificial Intelligence & Machine Learning is designed to provide students with an understanding of the data science of advanced algorithms and computational methods that underpin machine learning, AI-based systems and AI-driven technologies. 

The cutting-edge curriculum covers a range of topics, including developing intelligent systems, neural networks, natural language processing, computer vision, common machine learning tasks, pattern recognition, deep reinforcement learning, data visualization, robotics, adversarial networks and autonomous systems. In addition to academic rigor and real-world experience and applications, the program emphasizes ethical considerations and the societal impacts of cutting-edge AI technologies.

Armed with a world-class education of mathematical foundations and relevant skills at one of the field’s top schools, graduates of this AI program are well-prepared to become artificial intelligence and deep learning engineers and for artificial Intelligence careers in various industries, including technology, finance, healthcare, and transportation. The online Artificial Intelligence and Machine Learning degree program also lays a strong foundation of technical support for those interested in pursuing research or doctoral studies in these rapidly evolving fields.

Curriculum

The 100% online master’s program consists of 10 online MEng courses (three credit hours each), totaling 30 required credit hours. Its online learning environment offers synchronous and asynchronous learning options. This gives students who are typically working adults the flexibility to pursue an advanced degree at their convenience and from any location. 

Learn More About the Courses

EMSE 6769 Machine Learning for Engineers: Theory and practice of machine learning, leveraging open-source frameworks to explore the ideas, algorithms, and techniques. (3 credit hours)

EMSE 6820 Program and Project Management: Problems in managing projects; project management as planning, organizing, directing, and monitoring; project and corporate organizations; duties and responsibilities; the project plan; schedule, cost, earned-value and situation analysis; leadership; team building; conflict management; meetings, presentations, and proposals. (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 6505 Quantitative Foundations in 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. Sequence-to-sequence modeling. 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 6599 AI Capstone Project: Propose on a comprehensive AI project, applying acquired skills and knowledge from the program. Tackle real-world problems, design and implement effective solutions under faculty guidance. Demonstrate mastery in AI through project completion and presentation. (3 credit hours)

SEAS 8550 Artificial Intelligence: Law and 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

Our classes meet one night a week (Mondays, Tuesdays, Wednesdays, or Thursdays) for nine weeks from 6:30 to 9:50 PM Eastern time.  Students enroll in four 9-week sessions. There is also an optional fifth summer session (classes meet twice a week for four and half weeks). Students may begin their studies in any of the five sessions. Please see below for the dates of our upcoming sessions.

Session	Dates	Application Deadlines
Spring-1 2025	week of 1/6/25 – week of 3/3/25	12/1/2024
Spring-2 2025	week of 3/10/25 – week of 5/5/25	2/14/2025
Summer 2025	week of 5/27/25 – week of 6/23/25	5/1/2025
Fall-1 2025	week of 8/11/25 – week of 10/6/25	7/28/2025
Fall-2 2025	week of 10/13/25 – week of 12/8/25	9/29/2025

The course order is determined by advisors based on student progress toward completion of the curriculum. Course details will be provided to students via email approximately one month prior to the start of classes.

Tuition

We strive to deliver both a quality education and affordable master’s degree program. Tuition is $1,200 per credit hour for the 2024-2025 academic year. Tuition is billed at the beginning of each semester for the courses registered during that semester. A non-refundable tuition deposit of $495 is required when the applicant accepts admission. This deposit is applied to tuition and due the first semester. Our online graduate degree programs in engineering require no additional fees. We provide eBooks and software at no additional cost. 

Admissions Process

Review the Admissions Requirements 

Ideal candidates for the programs will meet the following requirements:

• Hold a bachelor's degree in engineering, computer science, mathematics, physics, or a closely related field from an accredited institution.
• Minimum grade point average of B (3.0 on a 4.0 scale) or higher.
  • Applicants with less than a 3.0 GPA may also apply and may be accepted conditionally based on a holistic review of application materials
• Grade of C or better in one course in college-level calculus and one course in college-level statistics. Applicants who do not meet this requirement in full but are otherwise qualified may be conditionally admitted and required to take an additional 3-credit hour course, EMSE 4197 — Special Topics: Quantitative Methods in Engineering Management, during the first year of graduate study.
• If you’re applying from outside the U.S., please see international student admissions information for additional requirements.

Note: GW considers a candidate’s entire background when reaching an admissions decision. Applicants who do not meet all the requirements may still be eligible for admission. Their records will be evaluated on a case-by-case basis. 

Apply for Admission and Submit Supporting Documents

Apply for Admission
 There is no application fee for any GW online engineering program. 

Complete application packets include:

• Resume or C.V.: Upload your up-to-date resume or C.V.
• Statement of Purpose: In an essay of 250 words or less, describe your academic objectives, research interests, and career plans. Also, discuss any other related qualifications not already mentioned, such as collegiate, professional, or community activities.
• Official Transcripts: Official transcripts are required from all institutions where a degree was earned. Transcripts should be sent to [email protected] (if sent electronically), or via mail to:
  Online Engineering Programs
  The George Washington University
  170 Newport Center Drive
  Suite 260
  Newport Beach, CA 92660
   
• Letters of Recommendation: Two (2) letters of recommendation are required, and one must be from a professional reference. Begin this process by filling out this letter of recommendation form. Within this form you will share the email address of the individual providing your recommendation. Once you complete your required section, Formstack will send that individual a link to the letter of recommendation form. A letter of recommendation is only considered official when it is sent directly from the individual providing the recommendation through Formstack. Submissions directly from applicants will not be accepted.
• GRE Scores: GRE scores are not required but if available, should be submitted to enhance your application.

Remain Engaged in the Admissions Process

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

View a previously recorded info-session