Online Master of Engineering in Artificial Intelligence and Machine Learning

We are now accepting applications for our summer and fall semester start dates. For more details on application deadlines and start dates, refer to the academic calendar.

Program Description

The online Master of Engineering in Artificial Intelligence and Machine Learning is designed to provide students with an understanding of the advanced algorithms and computational methods that underpin artificial intelligence and machine learning technologies. The program covers a range of topics, including neural networks, natural language processing, computer vision, deep learning, robotics, and autonomous systems. In addition to technical skills, the program emphasizes ethical considerations and the societal impacts of AI technologies.

Graduates of this program are well-prepared for careers in various industries, including technology, finance, healthcare, and transportation. The program also lays a strong foundation for those interested in pursuing research or doctoral studies in these rapidly evolving fields.

Curriculum

The program consists of 10 courses (three credit hours each), totaling 30 required credit hours. Its online structure offers synchronous and asynchronous learning options. This gives students the flexibility to study 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 Privacy and Organizational Issues in AI: Technological basis of ethics in AI. Differentiating humans from machines in AI. Key topics in privacy and ethics of AI, including intrinsic bias and the significance of models. AI and individual responsibility. Addressing legal and regulatory issues. (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 five 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
Summer 2024	week of 5/20/24 – week of 6/17/24	5/1/2024
Fall 1 2024	week of 8/12/24 – week of 10/7/24	7/31/2024
Fall 2 2024	week of 10/14/24 – week of 12/9/24	9/30/2024
Spring 1 2025	week of 1/6/25 – week of 3/3/25	12/13/2024
Spring 2 2025	week of 3/10/25 – week of 5/5/25	2/20/2025

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

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- (2.7 on a 4.0 scale) or higher.
  • Applicants with less than a 2.7 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, Docusign 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 DocuSign. 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