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Admission

Graduate Certificate in Artificial Intelligence and Machine Learning for Engineering

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Graduate Certificate in Artificial Intelligence and Machine Learning for Engineering

NEXT START DATE: Fall 2025
PRIORITY DEADLINE: TBD for 2025
APPLICATIONS WILL OPEN: Spring 2025

Get notified!

LOCATION

Online

DURATION

15 months part-time

TIMES

Mostly asynchronous

TOTAL COST

$18,000

Program highlights

The Graduate Certificate in Artificial Intelligence (AI) and Machine Learning (ML) for Engineering equips engineers to use modern data-driven AI and ML methods. This certificate can be completed independently or combined with other eligible data-intensive certificates in the College of Engineering to create a stacked Master of Science in Artificial Intelligence and Machine Learning for Engineering.

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AI & ML tools for engineering

Learn how to use AI and ML methods tailored towards physical, chemical, and engineered systems.

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Designed for working engineers

Online, part-time 18-credit graduate certificate with courses  that can be adapted to your skills and engineering discipline.

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Stackable towards a master’s degree

You can choose to combine the certificate with another eligible certificate to create a stacked master's degree.

Who is this program for?

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.

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Learning outcomes

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.

Courses

The Graduate Certificate in Artificial Intelligence and Machine Learning for Engineering is an online 18-credit graduate certificate. It includes:

  • 5-credit foundations course
  • 4-credit math course
  • 5-credit physics-informed machine learning course
  • 4 credits of 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.


Foundations of Machine Learning for Engineering

Foundational skills for using AI and ML methods. Math and coding skills, AI ethics, and an overview of applications in engineering.


Data-Driven Optimization

Optimization techniques used across modern engineering, including machine learning and control theory. Covers fundamentals, case studies, and deep-dive topics.


Physics-Informed Machine Learning

Machine learning algorithms applied to scientific and engineering problem solving. Includes an applied project.

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.

Applied Linear Algebra

This engineering math course covers how linear algebra can be applied to common machine learning techniques, especially those used for engineering and physics. Some of the major topics covered include model reduction, modal analysis, and how sparsity and statistics can be leveraged to describe physical systems. This course satisfies the certificate math requirement. Offered in Winter, starting in 2026. 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 and an applied project that incorporates skills learned throughout the certificate. This is a required course. Offered in Spring. 5 credits.

Certificate stackability

Students can choose to take the certificate independently or combine it with another eligible data-intensive certificate to create a Stacked Master of Science in Artificial Intelligence and Machine Learning for Engineering.

Admission requirements

Applicants need a 3.0 cumulative grade-point average on a 4-point scale from an accredited school and meet specific coursework requirements listed on the admissions page. To be considered for admission, applicants should submit a resume, statement of purpose, and unofficial/electronic transcripts.

How to apply

Application will open spring, 2025

Our next program starts fall 2025.

Get notified!

Featured instructors

Steve Brunton

Steve Brunton

Professor, Mechanical Engineering
Adjunct Professor, Applied Mathematics

Professor Brunton serves as the Director of the AI Center for Dynamics & Control and holds the position of Data Science Fellow at the eScience Institute. His research combines techniques in dimensionality reduction, sparse sensing, and machine learning for the 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 fluid dynamics, such as closed-loop turbulence control, as well as in neuroscience, medical data analysis, networked dynamical systems, and optical systems.

Nathan Kutz

Nathan Kutz

Professor, Electrical & Computer Engineering
Professor, Applied Mathematics

Professor Kutz is a distinguished scholar in applied mathematics, with extensive contributions to numerical methods, scientific computing, data analysis and interdisciplinary research. Throughout his career of several decades, he has significantly advanced mathematical theory while addressing practical challenges in fields such as nonlinear optics, fluid dynamics, neuroscience, and video and image processing. As an esteemed educator and researcher, Professor Kutz continues to inspire and mentor future generations of mathematicians and scientists.

For more information

Join our mailing list for future updates. Please contact us at ai4eng-advising@uw.edu if you have additional questions.