IE:6300 (56:230) Innovation
Science and Studies
Spring 2014
Course Description:
Innovation typology and sources;
Classical
innovation models; Measuring innovation; Innovation discovery from
data; Computational
intelligence in innovation; Innovation life-cycle. Introducing concepts, models,
algorithms, and tools for development of innovation
systems. Computational intelligence topics include data mining, expert
systems,
neural networks, particle swarm optimization, ant colony algorithms,
artificial
immune systems, sand their applications in innovation. Learning
research
methodologies and preparing research papers and reports.
Instructor
Andrew
Kusiak
2139 Seamans Center
Tel: 319 - 335
5934
Fax: 319 - 335 5669
Email address
Instructor's Office Hours and Place
1:30 PM - 3:00 PM, TTh
2139 Seamans Center
Graduate Research Assistant
Zijun
Zhang, PhD Student
GRA's Office Hours and Place
3221 Seamans Center
1:00 PM – 2:00 PM, MW or by appointment
Class Time
10:55 AM - 12:10 PM, TTh
Classroom
3231
Seamans Center
Textbook:
R.
Eberhart and Y. Shi, Computational
Intelligence: Concepts to Implementations, Morgan Kaufmann
(Elsevier),
2007.
Course Content
For literature search use these libraries:
Innovation News
IEEE Innovation
Institute
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Revised: July 26, 2011