Certificate in AI & Machine Learning for Engineering

The Graduate Certificate in Artificial Intelligence and Machine Learning for Engineering equips engineers to use modern data-driven AI and ML methods.

Skip Quicklinks and continue to page content

Related information

Admissions and cost

Request information

Also see

The Master of Science in AI & ML for Engineering

Get in touch

AI4Eng-advising@uw.edu

Program features

Flexible learning options

This program is offered mostly asynchronously. It can be completed fully online, part-time, in 9 months.

Application-focused

Learn how to apply modern AI and ML methods, particularly for applications with physical constraints, such as manufacturing, chemical processes, or robotics.

Stackable towards a master’s degree

You can complete the certificate independently or combine it with another eligible certificate to obtain the Master of Science in AI & Machine Learning for Engineering.

Cost

$18,000 (estimated)

Explore the certificate

This certificate is designed for engineers who want to apply modern AI and ML methods to their field, particularly for applications with physical constraints, such as manufacturing, chemical processes, or robotics. Students will advance their careers by building on their traditional engineering expertise and learn how to apply data-driven techniques to engineering use cases.

Not sure which program is right for you? Visit our AI/ML graduate programs at a glance.

Implement and evaluate AI & ML methods: choose and implement the appropriate AI and ML methods for specific engineering applications, and learn how to evaluate the results of using these methods.

Build foundational AI & ML skills: strengthen math and coding skills, creating a foundation that enables you to adapt to changing AI and ML tools throughout your career.

Communicate methods and results: practice and receive feedback on communicating your work using data visualization, verbal presentations, and written reports.

Designed for working engineers looking to enhance their skills or advance their careers, stacked degrees offer a flexible, strategic pathway with industry application in mind.

Students begin by enrolling in a stackable graduate certificate, then "stack" additional graduate certificates, and an applied capstone project to earn a master's degree.

The Certificate in AI and Machine Learning for Engineering is a key component of the Master of Science in AI & Machine Learning for Engineering.

All stackable certificates are available part-time.

The Graduate Certificate in Artificial Intelligence and Machine Learning for Engineering is an online 16-credit graduate certificate. It includes:

5-credit foundations course
4-credit math course
3-credit physics-informed machine learning course
2-credit AI & machine learning project
2 credits of seminar or electives

Except for the electives, these courses are designed in a modular format, combining required modules, core modules, and elective modules. This structure allows students to specialize in techniques based on their initial skill level and engineering discipline.

Course examples

Foundations of Machine Learning for Engineering: The first course in the certificate builds foundational skills for using artificial intelligence and machine learning techniques in engineering. This includes mathematical and coding skills, an introduction to types of artificial intelligence and machine learning algorithms, and an overview of how artificial intelligence and machine learning can be applied to engineering applications. Also includes a brief introduction to ethics in AI. This is a required course. Offered in Fall. 5 credits.

Data-Driven Optimization: Applied optimization is the backbone of modern data-driven modeling and machine learning. This course covers optimization techniques used across modern engineering, including in machine learning and control theory. This course covers both optimization fundamentals and deep-dives into relevant topics, such as convex vs. nonconvex optimization, constrained optimization, high-dimensional and stochastic techniques for big data, and computational techniques. This course satisfies the certificate math requirement. Offered in Winter. 4 credits.

Physics-Informed Machine Learning: This course covers core machine learning algorithms as they apply to scientific and engineering problem solving. Examples include how to enforce known, or partially known physics into machine learning algorithms and how to discover new physics with machine learning. Topics include physics-informed neural networks, digital twins, interpretable and generalizable models, and reinforcement learning. Coursework includes case studies that incorporates skills learned throughout the certificate. This is a required course. Offered in Spring. 3 credits.

Machine Learning for Engineering Project: This course covers core provides students the opportunity to apply skills learned during previous graduate work in our program. Students practice end-to-end implementation of learned methods to solve intermediate and advanced problems and evaluate their work from the perspectives of efficacy, accuracy, safety, and ethics. This is a required course. Offered in Spring. 2 credits.

Admission and cost

How to apply

Steps to apply

Create an account in the UW Graduate School’s online application portal. We recommend using a personal email address rather than a school or work email to ensure that you have an active secondary contact email address on file throughout your association with UW.

To start your application to the Graduate Certificate in AI & Machine Learning for Engineering, select ‘Graduate’ for your application type. Then, filter by campus (Seattle) and select "Engineering Grad Cert - AI & ML for Engineering (Stackable)" from the list of programs.

Profile Information: Take care to ensure this information is accurate, with particular attention to the questions regarding citizenship, residency, and whether you are a native English speaker.
Contact Information: Enter your current and permanent addresses.
Academic history: Report each higher education institution you have attended and your dates of attendance.

For each school, upload unofficial copies of your transcript and report your cumulative GPA. For support converting an international GPA to the 4-point scale, you may use Scholaro’s free calculator.
- For the school(s) where you have previously earned a degree, your documents must indicate the degree and when it was awarded. Transcripts and marksheets often indicate this information. If your transcript and degree statement were issued separately, then please combine your unofficial copies into one file before uploading.
- If your transcripts are in a language other than English, please have them translated and upload the translated versions in your application.

Official transcripts are not required during the application process; you will only have to submit official documents to UW if you accept an admission offer. At that time, you should provide your documents according to the Graduate School’s official transcript requirements.

1-page resume: including lists of relevant professional experience and contributions to the community through volunteering and service.
1-page Statement of Purpose: which should describe how this program would help the prospective student reach their professional goals in their current and/or potential future work. Make sure to elaborate on career goal alignment and potential future benefits to the community that you feel your work could provide.

Apply today

To apply to our AIML for Engineering certificate program, make sure to select "Engineering Grad Cert - AI & ML for Engineering (Stackable)" in the program drop down selection.

Admission requirements

The University of Washington's minimum criteria represent the requirements an applicant must meet to be eligible for admission to a graduate program.

Program-specific requirements

Undergraduate coursework in calculus, differential equations, linear algebra, and physics.
Undergraduate coursework or work experience with writing computer code in any language.
Strongly recommended: In previous degree, majored in engineering, physics, chemistry, or a closely related discipline. Other majors will be considered, if required prerequisite coursework above has been completed.

If you do not meet some or all of the minimum requirements, please use the personal statement to address these issues.

If you have been out of school for more than 5 years, or if your most recent degree is not in a closely related subject,
- Use your personal statement or resume to highlight how you have kept up your math and coding skills, for example through work or continuing education.
- If you do not use math and coding in your current work, consider taking a course at UW as a graduate non-matriculated student to brush up on skills.
If you received low grades in prerequisite courses or are missing prerequisite courses on transcripts, use your personal statement to explain how you have gained or strengthened these skills through your career or continuing education.
If you have not taken the prerequisite courses, consider taking them at UW as a non- matriculated student.

Prospective students not enrolled in a graduate program at the University of Washington must apply to the Graduate School as part of their application.

Prior degree

Applicants must hold the minimum equivalent of a four-year baccalaureate degree from a regionally accredited college or university in the United States or its equivalent from an accredited foreign institution. Holders of a Bologna bachelor’s degree must have a minimum of 180 European Credit Transfer System (ECTS) credits.

Holders of 3-year degrees may apply and be considered, but final approval from the Graduate School is required.
Alternatively, applicants may hold a master’s (MA, MSc, etc.), doctoral (PhD, DPhil, etc.), or professional degree (MD, JD, etc.) from a regionally accredited college or university in the United States or its equivalent from an accredited foreign institution.

Grade Point Average (GPA)

Applicants must have earned at least a 3.0 GPA (on a 4 point scale) for the credits earned at the institution awarding their bachelor’s degree or at least a 3.0 GPA (on a 4 point scale) for their last 90 graded quarter credits or 60 graded semester credits. If you earned additional credits from an accredited institution after your degree, you may include these credits in calculating your GPA.

For applicants who hold a master’s, doctoral, or professional degree, the total cumulative average from that degree course may be used.

English language proficiency

All applicants who are non-native speakers of English must enclose proof of their English proficiency in their application. For official UW Graduate School policy information, please refer to Graduate Policy 3.2: English Language Proficiency Requirements.

For more information, reference the UW Graduate Schools' minimum admission requirements. This program does not require the GRE. This program does not require the GRE.

This certificate program is not eligible for F1/J1 international students who require an I-20/DS-2019 to enroll at UW. Non-U.S. citizens in the U.S. who do not require an I-20/DS-2019 to study and have the ability to be a student, can be admitted to the certificate. Students wishing to complete the online certificate while remaining outside of the United States are eligible to apply. Students in that situation may want to contact ai4eng-advising@uw.edu to understand access options and requirements for synchronous participation.

Featured faculty

Steve Brunton

Professor, Mechanical Engineering
Adjunct Professor, Applied Mathematics

Steve Brunton is a Professor of Mechanical Engineering at the University of Washington. He serves as the Director of the NSF AI Institute in Dynamic Systems and is a Data Science Fellow at the eScience Institute. His research combines techniques in dimensionality reduction, sparse sensing, and machine learning for data-driven discovery and control of complex dynamical systems. Additionally, he develops adaptive controllers using machine learning within an equation-free framework. His work spans applications in many different fields.

Gian-Gabriel Palaci Garcia

Assistant Professor
Department of Industrial & Systems Engineering

Gian-Gabriel Garcia is an Assistant Professor of Industrial & Systems Engineering (ISE) at the University of Washington. His research interests are in the design, analysis, and optimization of data-driven frameworks at the intersection of optimization, machine learning, and artificial intelligence. His work is motivated by high-impact problems in medical decision-making, health policy, and healthcare operations. This work draws applications from many areas of healthcare, including concussion, cardiovascular disease, diabetes, maternal health, pediatric health, mental health, the opioid crisis, and surgical scheduling.

Michelle Hickner

Assistant Teaching Professor,
Department of Mechanical Engineering

Michelle Hickner is an Assistant Professor in Mechanical Engineering at the University of Washington. She has served in a variety of teaching roles at the University of Washington, focusing on hands-on and experiential learning. Her scholarly interests include engineering education, sensing and control in animal flight, and data-driven system identification. Her past work has included running the UW mechanical engineering composite shop, and research and development of devices for airborne particle handling.

Stefania Fresca

Assistant Professor
Department of Mechanical Engineering

Stefania Fresca is an Assistant Professor in Physics-based AI at the Department of Mechanical Engineering, University of Washington. She is also a visiting Professor at the Department of Computer Science at the University of Cambridge. Her research interests and expertise include scientific machine learning, reduced-order modeling, digital twins, structure-preserving neural networks, multiscale deep learning, and numerical approximation of PDEs, with several applications in engineering.

Follow AI & ML programs on social media

YouTube

Certificate in AI & Machine Learning for Engineering

Related information

Also see

Get in touch