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ME 570 Computational Intelligence (Fall Semester)
Introduction to
conventional AI topics, and recently surging intelligent optimization
schemes. From the theory of Neural Networks, to the scheduled cooling
in parameter optimization in SA. Inductive and Deductive decision
making, simulation of natural processes where nature is at her best :
The evolution. It is intended to cover a range of topics from classical
to modern computational intelligence.
This course is meant to let
graduate students acquire a basic insight into the emerging
methodologies in the so-called field of computational intelligence. The
students are expected to have a working knowledge in the fundamentals
of the area. They will be able to apply these optimization techniques,
with the average coding skills, in their respective fields of research. Simon Haykin, "Neural Networks, A Comprehensive Foundation", Prentice Hall, 1999. Fredric M. Ham, Ivica Kostanic, "Principles of Neurocomputing For Science & Engineering", Mc Graw-Hill, 2001.
T.
Munakata, "Fundamentals of the New Artificial Intelligence: Beyond
Traditional Paradigms", Springer-Verlag, 1998. Downloads * Machining ,
National
Int
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