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Crcd Experiences At The University Of Central Florida: An Nsf Project

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Conference

2004 Annual Conference

Location

Salt Lake City, Utah

Publication Date

June 20, 2004

Start Date

June 20, 2004

End Date

June 23, 2004

ISSN

2153-5965

Conference Session

Undergraduate Research & New Directions

Page Count

20

Page Numbers

9.348.1 - 9.348.20

Permanent URL

https://peer.asee.org/13194

Download Count

13

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Paper Authors

author page

Michael Georgiopoulos

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Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Session 1332

CRCD Experiences at the University of Central Florida: An NSF Project Michael Georgiopoulos*, Jose Castro*, Erol Gelenbe**, Ronald Demara*, Avelino Gonzalez*, Marcella Kysilka*, Mansooreh Mollaghasemi*, Annie Wu*, Ingrid Russell***

(*) University of Central Florida (**) University of Central Florida and Imperial College (***) University of Hartford

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.

1. Introduction

This paper reports on the progress of a grant supported by the National Science Foundation under the auspices of the program entitled Combined Research and Curriculum Development (CRCD). The title of the grant is “Machine Learning Advances for Engineering Education” and its purpose is to introduce Machine Learning research into the undergraduate science and engineering curriculum. Our proposal relies on two simple ideas to achieve its goals. The first idea is the introduction of machine learning modules in the sophomore and junior level required classes. A machine-learning module is a homework assignment that has a machine learning

Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition Copyright © 2004, American Society For Engineering Education

Georgiopoulos, M. (2004, June), Crcd Experiences At The University Of Central Florida: An Nsf Project Paper presented at 2004 Annual Conference, Salt Lake City, Utah. https://peer.asee.org/13194

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