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Description:
Typically, Computational Intelligence is used as an umbrella term for the fields of
artificial neural nets (ANN), fuzzy systems (FS) and evolutionary computation (EC).
This course offers a thorough introduction into all three fields.
Foundations of ANN: McCulloch-Pitts nets, perceptrons, Hopfield nets,
supervised and unsupervised learning, backpropagation.
Foundations of FS: Fuzzy sets, fuzzy numbers, fuzzy logic, fuzzy reasoning.
Foundations of EC: Algorithmic basics, parameterization, analysis methods, limits of deployment.
Students should get an overview about the different aspects of Computational Intelligence.
They should be familiar with the essential elements in all three fields (ANN, FS, EC) and
able to deploy and adapt these methods for real applications. Moreover, they should be
able to assess when to deploy these methods and when not.
Slides:
11.10.17
Artificial Neural Networks I
18.10.17
Artificial Neural Networks II
25.10.17
Artificial Neural Networks III
08.11.17
Artificial Neural Networks IV
(slide #5 corrected)
15.11.17
Fuzzy Systems I
22.11.17
Fuzzy Systems II
29.11.17
Fuzzy Systems III
06.12.17
Fuzzy Systems IV
13.12.17
Fuzzy Systems V
20.12.17
Evolutionary Algorithms I
10.01.18
Evolutionary Algorithms II
17.01.18
Evolutionary Algorithms III
24.01.18
Evolutionary Algorithms IV
31.01.18
Swarm Intelligence
Literature:
A.E. Eiben and J.E. Smith: Introduction to Evolutionary Algorithms. Corrected 2nd printing. Springer 2007.
Raul Rojas: Neural Networks - A Systematic Introduction. Springer 1996. Available online.
G.J. Klir und B. Yuan: Fuzzy Sets and Fuzzy Logic. Prentice Hall 1995.
F. Höppner, F. Klawonn, R. Kruse und T. Runkler: Fuzzy Cluster Analysis. Wiley 1999.
Amit Konar: Computational Intelligence: Principles, Techniques and Applications. Springer 2005.
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