| Dates: |
|
|
| Wednesday |
10:15 - 11:45 am |
OH14 - E23
|
| First Lecture: |
Wednesday, 15-Oct-2025 |
News:
- 21-Jul-2025
The written exams will take place on
| - Monday, | 09-Feb-2026, | 1:00pm - 2:30pm |
| - Friday, | 27-Mar-2026, | 4:00pm - 5:30pm |
Tutorial: N.N. (M.Sc.), N.N. (B.Sc.)
Instruction Language: English.
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, RBF nets, Hopfield nets, convolutional neural nets (CNN), recurrent nets.
Foundations of FS: Fuzzy sets, fuzzy numbers, fuzzy logic, fuzzy reasoning, fuzzy control, fuzzy clustering.
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:
Will appear right here in due time.
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.
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning. MIT Press 2017.
- 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.