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Session 2432

A CRCD Experience: Integrating Machine Learning Concepts into Introductory Engineering and Science Programming Courses

M. Georgiopoulos, I. Russell, J. Castro, A. Wu, M. Kysilka, R. DeMara, A.Gonzalez, E. Gelenbe, M. Mollaghasemi University of Central Florida/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 describe the first phase of the project, that of the integration of Machine Learning concepts into introductory engineering and science programming courses through appropriately designed programming projects.

1. Introduction

Machine Learning is concerned with building computer systems that have the ability to improve their performance in a given domain through experience. In the last decade there has been an explosion of research in Machine Learning. A contributing factor in this growth is that traditionally independent research communities in symbolic Machine Learning, computational learning theory, neural networks, genetic algorithms, statistics, and pattern recognition have achieved new levels of collaboration. The outcome has been a plethora of results in Machine Learning emerging from all of these research communities working synergistically. The second reason for the explosive growth is that Machine Learning has been applied successfully to a

Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright © 2003, American Society For Engineering Education

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DeMara, R., & Gonzalez, A., & Wu, A., & Castro, J., & Russell, I., & Mollaghasemi, M., & Kysilka, M., & Gelenbe, E., & Georgiopoulos, M. (2003, June), A Crcd Experience: Integrating Machine Learning Concepts Into Introductory Engineering And Science Programming Courses Paper presented at 2003 Annual Conference, Nashville, Tennessee. 10.18260/1-2--12117