Program Overview

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Program Overview

The Graduate Certificate in Artificial Intelligence (AI) and Machine Learning (ML) for Engineering is designed to equip engineers with the skills 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.

Program Highlights

• The program focuses on AI and ML tools for engineering, teaching students how to use AI and ML methods tailored towards physical, chemical, and engineered systems.
• It is designed for working engineers, offering an online, part-time 16-credit graduate certificate with courses that can be adapted to the student's skills and engineering discipline.
• The certificate is stackable towards a master’s degree, allowing students to combine it 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 learning how to apply data-driven techniques to engineering use cases.

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 students to adapt to changing AI and ML tools throughout their career.
• Communicate methods and results: Practice and receive feedback on communicating work using data visualization, verbal presentations, and written reports.

Courses

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

Course Details

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 and an applied project that incorporates skills learned throughout the certificate. This is a required course. Offered in Spring. 4 credits.

Machine Learning for Engineering Project

This course provides students the opportunity to apply skills learned during previous graduate work in the 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.

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. This certificate can also be stacked as part of the Master of Engineering in Multidisciplinary Engineering degree.

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. To be considered for admission, applicants should submit a resume, statement of purpose, and unofficial/electronic transcripts.

Featured Instructors

• Steve Brunton: Professor, Mechanical Engineering, and Adjunct Professor, Applied Mathematics. His research combines techniques in dimensionality reduction, sparse sensing, and machine learning for the data-driven discovery and control of complex dynamical systems.
• Nathan Kutz: Professor, Electrical & Computer Engineering, and Professor, Applied Mathematics. He is a distinguished scholar in applied mathematics, with extensive contributions to numerical methods, scientific computing, data analysis, and interdisciplinary research.
• Michelle Hickner: Assistant Teaching Professor, Department of Mechanical Engineering. Her scholarly interests include engineering education, sensing and control in animal flight, and data-driven system identification.

Program Details

• Location: Online
• Duration: 9 months part-time
• Times: Mostly asynchronous
• Total Cost: $16,480
• Next Start Date: Fall 2026
• Deadline: June 1, 2026
• Applications Open: January 2026

This program was developed with funding support from The Boeing Company.