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

How to write?

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Revised:  July 26, 2011