Asee peer logo

Interdisciplinary Course On Neural Networks At The Graduate Level

Download Paper |

Conference

1997 Annual Conference

Location

Milwaukee, Wisconsin

Publication Date

June 15, 1997

Start Date

June 15, 1997

End Date

June 18, 1997

ISSN

2153-5965

Page Count

8

Page Numbers

2.255.1 - 2.255.8

Permanent URL

https://peer.asee.org/6642

Download Count

31

Request a correction

Paper Authors

author page

Fahmida N. Chowdhury

Download Paper |

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

Session 2432

Interdisciplinary Course on Neural Networks at The Graduate Level

Fahmida N. Chowdhury Michigan Technological University, Houghton, MI 49931

1 Motivation For some areas of science and engineering education it is increasingly important to move beyond traditional departmental boundaries. Neural networks is one such field, because even though it was developed largely by electrical and computer engineers, its applications are now very widespread. It has become a truly interdisciplinary area of study, research, and applications. Neural networks have found applications in fields ranging from medical diagnostics to economic forecasting, not to mention all areas of engineering. However, formal courses at the graduate level have been limited mostly to electrical engineering departments. Because of the interdisciplinary nature of the applications, I decided to develop and teach an interdisciplinary course on Neural Networks. This course was offered in the Spring of 1996, and it was the first of its kind at MTU. In the paper, I describe the experience, with all its positive and negative aspects.

2 Student Composition and Course Structure The students who enrolled were from electrical engineering, chemical engineering, environmental engineering, computer science, and mathematics departments. They were graduate students, with the exception of one senior from the EE dept. who en- rolled with special permission. With such a varied student body, the task of choosing course material was nontrivial, to say the least. The students had different levels of backgrounds in mathematics, programming, and physics. Since the goal was to ac- comodate the students from different departments, I decided to plan the course with less structure than usual courses. The following schedule was adopted:

l The first 5 weeks were spent on theoretical foundations and their computer implementations:

Chowdhury, F. N. (1997, June), Interdisciplinary Course On Neural Networks At The Graduate Level Paper presented at 1997 Annual Conference, Milwaukee, Wisconsin. https://peer.asee.org/6642

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 1997 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015