Development of a Computational Intelligence Course for Undergraduate and Graduate Studies
This paper presents the design, implementation and experiences of a new three hour experimental course taught for a joint undergraduate and graduate class at the University of Missouri-Rolla, USA. This course is unique in the sense that it covers the four main paradigms of Computational Intelligence (CI) and their integration to develop hybrid algorithms. The paradigms covered are artificial neural networks (ANNs), evolutionary computing (EC), swarm intelligence (SI) and fuzzy systems (FS). While individual CI paradigms have been applied successfully to solve real-world problems, the current trend is to develop hybrids of paradigms, since no one paradigm is superior to the others in all situations. In doing so, we are able capitalize on the respective strengths of the components of the hybrid CI system and eliminate weakness of individual components. This course is an introductory level course and will lead students to courses focused in depth in a particular paradigm (ANNs, EC, FS, SI). The idea of an integrated course like this is to expose students to different CI paradigms at an early stage in their degree program. The paper presents the course curriculum, tools used in teaching the course and how the assessments of the students' learning were carried out in this course.
G. K. Venayagamoorthy, "Development of a Computational Intelligence Course for Undergraduate and Graduate Studies," ASEE Annual Conference & Exposition, American Society for Engineering Education (ASEE), Jun 2005.
Electrical and Computer Engineering
Keywords and Phrases
Design Engineering; Engineering Education; Fuzzy Systems; Neural Networks
Article - Conference proceedings
© 2005 American Society for Engineering Education (ASEE), All rights reserved.
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