RET Site: Machine Learning and Smart System Design

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

Due to increased automation of manufacturing processes, industry demand for workers with knowledge and skills related to industrial automation is high. In addition, increasingly widespread deployment of high-speed sensors in the field allows vast amounts of data to be collected. Machine learning techniques can be applied to these data to enable automated systems to adapt to changing conditions—that is, to become smart systems. This RET site will provide opportunities for high school faculty to learn about machine learning and smart systems with emphasis on applications and careers related to industrial automation; to work on related research projects; and to develop and share new instructional resources. The program will strengthen education in machine learning and smart system design and ultimately strengthen the U.S. workforce and U.S. economic competitiveness. Students who choose careers in automation will also benefit; entry-level jobs related to automation pay well and often do not require a four-year degree. This RET site will provide opportunities for high school teachers to collaborate with faculty and student researchers in the College of Engineering at Texas A&M University. Program goals include: 1) provide teachers opportunities for professional development in machine learning and smart system design; 2) enrich their teaching of engineering concepts; 3) provide opportunities for them to network with industry mentors, and university faculty and student researchers; 4) equip them to encourage, guide and stimulate their students’ interest in careers in the program’s theme area; 5) encourage them to disseminate what they learn to other teachers in the field; and 6) develop long-term collaborative relationships between the project team, participating instructors and their institutions, and industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.