Are you an engineer looking to stay ahead in a rapidly evolving technological landscape? A Machine Learning Degree could be the key to unlocking new career heights and expanding your skill set. This flexible master’s program is meticulously crafted for engineering professionals aiming to swiftly acquire advanced skills in Artificial Intelligence (AI) and Machine Learning (ML), and to effectively apply these cutting-edge tools within diverse engineering domains.
Program Advantages
Flexible Learning for Working Professionals: Designed with the needs of working engineers in mind, our machine learning degree offers complete online learning options for part-time students, ensuring a balanced approach to career and education. A full-time study option is also available for those who wish to immerse themselves fully in the program.
AI and ML Tools Tailored for Engineering Challenges: Leverage your existing engineering foundation to master and implement AI and ML tools specifically designed to tackle complex engineering problems. This machine learning degree emphasizes the practical application of AI and ML in real-world engineering scenarios.
Customize Your Machine Learning Degree: Our stackable master’s degree structure empowers students to personalize their educational journey. You can tailor your coursework to align with your specific engineering discipline and career aspirations, making this machine learning degree a highly adaptable and relevant qualification.
Who Should Pursue This Machine Learning Degree?
This machine learning degree is ideally suited for engineers who are driven to propel their careers forward by integrating contemporary AI and ML methodologies into their professional practice. It’s especially beneficial for applications constrained by physical parameters, such as advanced manufacturing, intricate chemical processes, or sophisticated robotics systems.
Beyond gaining fundamental AI and ML competencies applicable across all engineering fields, students will receive specialized, domain-specific training. This ensures graduates are proficient in state-of-the-art AI and ML techniques directly relevant to their chosen area of engineering expertise, solidifying the value of their machine learning degree.
Stay informed about our program updates – join our mailing list today.
Core Learning Outcomes of the Machine Learning Degree
Implement Advanced AI & ML Techniques
Graduates of this machine learning degree will be adept at selecting and implementing the most appropriate AI and ML methods for a wide array of engineering applications. A key focus is on evaluating the effectiveness and outcomes of these methods in practical contexts.
Establish Foundational AI & ML Expertise
The curriculum is designed to enhance your mathematical and coding proficiencies, building a robust foundation. This ensures that as a graduate with a machine learning degree, you are equipped to adapt to the ever-changing landscape of AI and ML tools throughout your professional trajectory.
Understand the Ethical Dimensions of AI & ML in Engineering
This machine learning degree emphasizes the responsible application of AI and ML, guided by engineering ethics. Students will learn to critically assess both the advantages and limitations of these technologies within discipline-specific environments, ensuring ethical and effective deployment.
The Flexible Stacked Master’s Degree Advantage
Designed specifically for working engineers seeking career advancement or skill enhancement, our stacked degree program offers a strategic and flexible educational pathway focused on real-world industry applications. This machine learning degree is structured to fit seamlessly into your professional life.
The stacked format allows students to begin with a graduate certificate, progressively ‘stacking’ additional graduate certificates, culminating in an applied capstone project to earn the full master’s machine learning degree. All stackable certificates are available on a part-time basis, with convenient online options to suit your schedule.
Components of the Machine Learning Degree Program
Certificates can be pursued individually as a part-time student or concurrently for full-time enrollment, offering maximum flexibility in achieving your machine learning degree.
Component 1
Graduate Certificate in Artificial Intelligence (AI) and Machine Learning (ML) for Engineering (Required Foundation)
Note: For most students, starting with the Graduate Certificate in AI & ML for Engineering is recommended as the initial step towards their machine learning degree. An exception applies to those specializing in AI/ML-Driven Molecular and Process Engineering, where the chemical engineering certificate should be completed first.
Component 2
Choose one discipline-specific, data-intensive graduate certificate from the following options to specialize your machine learning degree:
- Graduate Certificate in Data Analytics for Systems Operations, offered by the Department of Industrial and Systems Engineering.
- Graduate Certificate in AI/ML-Driven Molecular and Process Engineering, offered by the Department of Chemical Engineering – expected launch Fall 2025.
- Graduate Certificate in Data-Driven Dynamical Systems & Control for Engineering, offered by the Department of Mechanical Engineering.
Component 3
Applied Capstone Project: A two-quarter capstone sequence where you will apply the AI and ML techniques acquired throughout your machine learning degree. This project allows for the exploration of complex and innovative use cases, enhancing both your technical expertise and project management capabilities, crucial for a successful machine learning degree outcome.
Admission Information for the Machine Learning Degree
Applications for the Master of Science in Artificial Intelligence and Machine Learning for Engineering, leading to your machine learning degree, are now being accepted for Fall 2025, with an application deadline of July 1st, 2025.
Apply for Your Machine Learning Degree Now!
Admission Requirements: Applicants should hold a bachelor’s degree from an accredited institution with a minimum GPA of 3.0 on a 4.0 scale, and fulfill specific coursework prerequisites detailed on our admissions page. The application process requires a resume, statement of purpose, one letter of recommendation, and unofficial academic transcripts. Standardized tests are not required for admission to this machine learning degree program.
Important Note for International Applicants: This machine learning degree program is currently not available for F1/J1 international students who need an I-20/DS-2019 to enroll at the University of Washington. Non-U.S. citizens already in the U.S. with appropriate visa status may be eligible. Students residing outside the U.S. interested in online completion should contact [email protected] to discuss access options for this machine learning degree.
Detailed Admissions & Application Information
Learn more about admission requirements for the Machine Learning Degree
Program Costs & Financial Aid
Explore investment details and financial support options for your Machine Learning Degree
Meet Our Distinguished Faculty
Steve Brunton
Professor, Mechanical Engineering & Adjunct Professor, Applied Mathematics
Professor Brunton is the Director of the AI Center for Dynamics & Control and a Data Science Fellow at the eScience Institute. His research expertise lies in dimensionality reduction, sparse sensing, and machine learning for data-driven discovery and control of complex systems. His work spans diverse applications, enhancing the curriculum of our machine learning degree.
Ashis Banerjee
Associate Professor, Industrial & Systems Engineering & Associate Professor, Mechanical Engineering
Professor Banerjee leads the SMARTS Lab and is affiliated with the Boeing Advanced Research Center. His research is centered on automated decision-making for cyber-physical systems, utilizing optimization, machine learning, and stochastic modeling, all vital components of a comprehensive machine learning degree.
David Beck
Research Associate Professor, Chemical Engineering
Professor Beck is the Director of Research at the eScience Institute and Director of the Scientific Software Engineering Center. His research focuses on molecular data science applications across energy, health, and environment, contributing significantly to the depth and breadth of our machine learning degree.
Get More Information
Join our mailing list for the latest updates. For any further inquiries, please contact us at [email protected]. We are here to help you take the next step towards your machine learning degree.
