The University of Washington’s Master of Science in AI & Machine Learning for Engineering is a flexible, stackable degree for engineers who want to apply modern AI and machine learning methods to physical systems. Students combine a required AI and machine learning certificate with one discipline-specific certificate and an applied capstone.
Quick Facts
| Item | Details |
|---|---|
| School | University of Washington |
| Location | Seattle, Washington |
| Degree | Master of Science in AI & Machine Learning for Engineering |
| Credits | Approximately 39–42 quarter credits, depending on certificate choice |
| Format | Online; campus and hybrid specialization options are also available |
| Estimated Tuition | $40,375–$43,088 for 2025–26 |
| Typical Length | 2–4 years; full-time and part-time routes available |
| GRE | Not required |
| AI/ML Focus | Applied machine learning for engineering, physical systems, optimization, and discipline-specific applications |
Curriculum
The degree uses UW’s stacked-degree model. Every student completes three components: the Graduate Certificate in AI & Machine Learning for Engineering, one approved engineering-focused certificate, and an 8-credit capstone sequence.
The required AI/ML certificate carries 16 quarter credits. It develops the mathematical, programming, optimization, and modeling skills needed to use machine learning in engineering work.
- Foundations of Machine Learning for Engineering introduces AI and ML algorithms, coding, mathematical foundations, applications, and AI ethics.
- Data-Driven Optimization covers convex and nonconvex optimization, constraints, stochastic methods, and high-dimensional data.
- Physics-Informed Machine Learning examines physics-informed neural networks, digital twins, reinforcement learning, and interpretable models.
- Machine Learning for Engineering Project asks students to implement and assess AI or ML methods while considering accuracy, safety, efficacy, and ethics.
Discipline-Specific Certificate Options
Students select a second certificate that connects AI and data methods to a particular engineering domain. Delivery format and credit requirements vary by selection.
| Certificate | Focus | Available Format |
|---|---|---|
| Data-Driven Dynamic Systems and Control for Engineering | System identification, sensing, control, and robotics | Online |
| Data Analytics for Systems Operations | Analytics, operations, and industrial systems | Online or campus |
| Modern AI Methods | Modern AI techniques through the Allen School of Computer Science & Engineering | Campus |
| AI for Materials Engineering | AI and data science applications in materials engineering | Online or campus |
| Human Centered AI | AI systems centered on human needs and interaction | Campus |
The available certificate combinations let students shape the degree around robotics, manufacturing, operations, materials, computing, or human-centered system design. An online student can pair the required AI/ML certificate with an online option in controls, systems operations, or materials engineering.
Credits and Program Length
The exact degree total varies because the discipline-specific certificates have different credit requirements. Based on UW’s published components, students should plan for approximately 39–42 quarter credits: 16 credits in the required AI/ML certificate, about 15–18 credits in the selected certificate, and 8 capstone credits.
UW states that most students take one year or more to finish each certificate and two to four years to complete the degree. Part-time students normally complete one certificate before moving into the second. Full-time students may take the certificates concurrently.
Tuition and Cost
UW estimates total tuition at $40,375–$43,088 for 2025–26. The final amount depends on the second certificate because its tuition rate and credit total may differ.
The estimate combines both certificates with the 8-credit capstone. UW lists the 2025–26 capstone rate as $1,030 per credit. Students also pay quarterly university fees that vary with their course load, so those charges can increase the final cost.
For added context, the required AI/ML certificate lists a 2026–27 rate of $1,061 per credit, or about $16,976 for 16 credits before fees. Tuition may rise during a multi-year program, which makes UW’s published total a planning estimate rather than a fixed price.
Admissions
Applicants need a bachelor’s degree from an accredited institution and must meet the requirements for both certificate components. UW requires at least a 3.0 GPA based on the bachelor’s degree or the applicant’s last 90 quarter credits or 60 semester credits.
The required AI/ML certificate expects prior coursework in calculus, differential equations, linear algebra, and physics. Applicants also need programming coursework or work experience in any language. UW strongly recommends a background in engineering, physics, chemistry, or a related discipline, but it considers other majors that meet the prerequisites.
- Unofficial transcripts
- A one-page resume
- A one-page statement of purpose
- One letter of recommendation
- English proficiency evidence when required
The program does not require the GRE. Full-time students submit applications for the master’s degree and both certificates at the same time. Part-time students first enter the required AI/ML certificate and apply to the master’s degree when they apply for, or gain admission to, the second certificate.
Online, Hybrid, and Campus Options
UW supports a fully online path for part-time students. The required AI/ML certificate is mostly asynchronous and fully online, and several second-certificate choices also support online study. Some courses may include scheduled participation, so students outside the United States should confirm time-zone expectations with the program.
Students who want access to Modern AI Methods or Human Centered AI must attend those certificates on campus. Other certificate choices may be available online, on campus, or in both formats.
Machine Learning and AI Focus
This is a strong fit for the site because machine learning and AI form the degree’s core rather than a small elective component. Required study covers machine learning foundations, optimization, physics-informed models, neural networks, reinforcement learning, responsible AI, and an applied ML project.
The degree places unusual emphasis on engineering systems with physical constraints. Students learn to choose, implement, and evaluate AI methods for settings such as robotics, manufacturing, chemical processes, materials, sensing, and control.
Capstone
Students complete an 8-credit, two-quarter group capstone sequence. The project requires teams to apply methods from both certificates to a substantial or novel engineering problem while developing project management and technical communication skills.
Who Is This Program Best For?
This program best serves working engineers who already have calculus, linear algebra, differential equations, physics, and coding preparation. It is especially relevant for professionals who want to add AI and ML skills without leaving an engineering application area behind.
Students who want a general computer science degree or a research-focused thesis program may prefer another option. UW’s curriculum is professional, applied, interdisciplinary, and built around certificates plus a team capstone.
Bottom Line
The University of Washington offers a direct and meaningful AI and machine learning graduate degree with several engineering specializations. Its strongest features are the fully online route, the applied curriculum, and the option to match ML training with a technical domain. The tradeoffs are the variable 39–42-credit structure and an estimated total cost above $40,000.
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