Michael Georgiopoulos, Ronald F. DeMara, Avelino J. Gonzalez, Annie S. Wu, Mansooreh Mollaghasemi, Jimmy Secretan, Carthik A. Sharma
University of Central Florida
Erol Gelenbe
Imperial College, London SW7 2AZ, UK
Ayman J. Alnsour
Al-Isra Private University, Amman, Jordan

"A Sustainable Model for Integrating Current Topics in Machine Learning Research into the Undergraduate Curriculum"
accepted to IEEE Transactions on Education

This paper presents an integrated research and teaching model that has resulted from an NSF-funded effort to introduce results of current Machine Learning research into the engineering and computer science curriculum at the University of Central Florida (UCF). While in-depth exposure to current topics in Machine Learning has traditionally occurred at the graduate level, the model developed affords an innovative and feasible approach to expanding the depth of coverage in research topics to undergraduate students. The model has been self-sustaining as evidenced by its continued operation during the years after the NSF grant’s expiration, and is transferable to other institutions due to its use of modular and faculty-specific technical content. This model offers a tightly-coupled teaching and research approach to introducing current topics in Machine Learning research to undergraduates, while also involving them in the research process itself. The approach has provided new mechanisms to increase faculty participation in undergraduate research, has exposed approximately 15 undergraduates annually to research at UCF, and has effectively prepared a number of these students for graduate study through active involvement in the research process and co-authoring of publications.

Index Terms—Curriculum development, integrated research and teaching, machine learning, team teaching models, undergraduate research experiences