Knowledge Transfer
Curriculum & Education Committee
Our overarching goal is to design and implement a comprehensive education and training plan that integrates machine learning and artificial intelligence seamlessly into undergraduate and graduate engineering curriculum. This will involve identifying existing and desired skills and objectives and developing modular curriculum content to include in existing and new courses. This content will be deployed within our institute and will be available more broadly for other institutions and industry partners.
Committee Goals & Milestones
- Take stock of existing courses and materials available across this institute.
- All PIs list courses they teach and that they would like to teach
- Take stock of what skills and courses are missing. Analyze data systematically to identify near-term opportunities
- Poll institute for desired skills and content, creating catalog of what is available/needed
- Focus on 1-2 courses to design and implement as a template model for remaining material
- Finalize comprehensive plan for remaining material development and deployment, including which instructors develop which modules
- Develop measurable assessments for how effective material is for increasing student learning and reducing instructor effort
- Begin testing deployment of modular curriculum with industry partners
- Finish developing/deploying modules and courses and integrating into existing and new courses
- Roll out new courses across institutions
- Develop certificates and masters degree options within each university. Degrees are stackable from certificates, certificates are stackable from courses, and courses are stackable from flexible modules, hackathons, and capstones.
- Deploy modular curriculum with industry partners
Committee Members
Steve Brunton (UW)
Chair
Katie Banner (MSU)
Floris van Breugel (UNR)
Bree Cummins (MSU)
Lauren Lederer (UW)
Aditya Nair (UNR)
June Zhang (UH)
