Learn about strategies for implementing machine learning in the classroom from a National Science Foundation Research Experiences for Teachers lesson in this IEEE Education Society Seminar.
IEEE Education Society Seminar: Translating a Machine Learning Globally Based-Project into the Classroom
Presented by Anna Haywood, Glendale Community College, and Seckin Demir, ASU
Friday, April 29, 2022
Attend on Zoom
Last Summer 2021, Glendale Community College Professor Anna Haywood participated in the NSF Research Experiences for Teachers, or RET, on Sensors and Machine Learning. The goal of the program was to examine effective ways of translating groundbreaking research at the university level from ASU into lesson plans at the K-12 and community college level. The original engineering challenge is to further reduce and potentially eliminate sea turtle and other incidental megafauna bycatch from fishing nets while retaining the targeted fish. To this end, ASU BestLab has designed a “Smart Net” cyber-physical system with machine learning algorithms for species detection (recognition and location). This talk explores how this Global Community Based project incorporating machine learning has been translated into a developmental math classroom.
About the speaker
Anna Haywood is a professor of mathematics at Glendale Community College. She holds the following degrees from Arizona State University: BSE in electrical engineering, MS in bioengineering and a doctorate in mechanical engineering. Haywood joined the NSF Research Experiences for Teachers on Sensors and Machine Learning program in 2021. From RET, she has been actively utilizing the STEM lesson plans developed in Fall 2021 and continues this Spring 2022.
Seckin Demir received the bachelor’s and master’s degrees in electrical and electronics engineering from Bilkent University, Ankara, Turkey, in 2014 and 2017, respectively. Between 2014 and 2019, he worked as a computer vision scientist with ASELSAN Inc. He is currently a doctoral student and graduate research assistant in the School of Electrical, Computer and Energy Engineering at Arizona State University. His research interests include computer vision, image processing and machine learning.