Interdisciplinary Course On Neural Networks At The Graduate Level

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.