Title

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

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.

Publication Date

10-25-2004

Publication Title

ASEE Annual Conference Proceedings

Number of Pages

2489-2508

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

5444237153 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/5444237153

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