News:
Examinations:- 1st Exam Date:
Tuesday, 12-Feb-2019, 8:15 - 9:45 am.Location: HG II, HS 1.
Results: can be enquired via BOSS or responsible examination office.
Grading System: download
Post-exam Review:Tuesday, 26-Feb-2019, 3:00 - 4:00 pm. Room: OH 14, E 04.
- 2nd Exam Date:
Monday, 18-Mar-2019, 11:15 - 12:45 am. Location: HG II, HS 5.
Results: can be enquired via BOSS or responsible examination office.
Grading System: download
Post-exam Review:Monday, 29-April-2019, 3:00 - 4:00 pm. Room: OH 14, E04.
Tutorial: Marius Bommert (M.Sc.), LS 11 (web pages)
Instruction Language: English.
For a German version of this web page please click the 'Deutsch' button at the top right corner.
Written Exams: (final schedule)
1st Exam Date: Tuesday, 12-Feb-2019, 8:15am - 9:45am, HG II, HS 1.
2nd Exam Date: Monday, 18-Mar-2019.
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.
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:
The slides about organizational issues are in the first set of slides (10.10.18) below.
| 10.10.18 | Artificial Neural Networks I |  |
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| 17.10.18 | Artificial Neural Networks II | |
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| 24.10.18 | Artificial Neural Networks III | |
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| 31.10.18 | Fuzzy Systems I | |
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| 07.11.18 | Fuzzy Systems II | |
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| 14.11.18 | Fuzzy Systems III | |
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| 21.11.18 | Fuzzy Systems IV | |
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| 05.12.18 | Evolutionary Algorithms I | |
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| 12.12.18 | Evolutionary Algorithms II | |
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| 19.12.18 | Evolutionary Algorithms III | |
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| 09.01.19 | Evolutionary Algorithms IV | |
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| 16.01.19 | Swarm Intelligence | |
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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.