M. Georgiopoulos, J. Castro, E. Gelenbe, R. F. DeMara, A. J. Gonzalez, M. 
Kysilka, M. Mollaghasemi, and A. S. Wu, 
"CRCD Experiences at the University of Central Florida: An NSF Project," in 
Proceedings of the 2004 American Society for Engineering Education Annual 
Conference and Exposition (ASEE'04), pp. 2432: 1 - 23, Salt Lake City, Utah, 
U.S.A., June 20 - 23, 2004.

Abstract:
Machine Learning has traditionally been a topic of research and instruction in 
computer science and computer engineering programs. Yet, due to its wide 
applicability in a variety of fields, its research use has expanded in other 
disciplines, such as electrical engineering, industrial engineering, civil 
engineering, and mechanical engineering. Currently, many undergraduate and 
first-year graduate students in the aforementioned fields do not have exposure 
to recent research trends in Machine Learning. This paper reports on a project 
in progress, funded by the National Science Foundation under the program 
Combined Research and Curriculum Development (CRCD), whose goal is to remedy 
this shortcoming. The project involves the development of a model for the 
integration of Machine Learning into the undergraduate curriculum of those 
engineering and science disciplines mentioned above. The goal is increased 
exposure to Machine Learning technology for a wider range of students in science 
and engineering than is currently available. Our approach of integrating Machine 
Learning research into the curriculum involves two components. The first 
component is the incorporation of Machine Learning modules into the first two 
years of the curriculum with the goal of sparking student interest in the field. 
The second is the development of new upper level Machine Learning courses for 
advanced undergraduate students. The paper will focus on the details of the 
integration of a machine learning module (related to neural networks) applied to 
a Numerical Analysis class, taught to sophomores and juniors in the Engineering 
Departments at the University of Central Florida.
Furthermore, it will report results on the assessment and evaluation of the 
effectiveness of the module by the students taking the class. Finally, based on 
the assessment results some conclusions will be drawn regarding the potential of 
the modules in attracting undergraduate students into research, specifically 
machine-learning research.