Title

A Sustainable Model for Integrating Current Topics in Machine Learning Research Into the Undergraduate Curriculum

Authors

Authors

M. Georgiopoulos; R. F. DeMara; A. J. Gonzalez; A. S. Wu; M. Mollaghasemi; E. Gelenbe; M. Kysilka; J. Secretan; C. A. Sharma;A. J. Alnsour

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

IEEE Trans. Educ.

Keywords

Curriculum development; integrated research and teaching; machine; learning; team teaching models; undergraduate research experiences; Education, Scientific Disciplines; Engineering, Electrical & Electronic

Abstract

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 coauthoring of publications.

Journal Title

Ieee Transactions on Education

Volume

52

Issue/Number

4

Publication Date

1-1-2009

Document Type

Article

Language

English

First Page

503

Last Page

512

WOS Identifier

WOS:000271490000006

ISSN

0018-9359

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