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Günter Rudolph: Lecture Introduction to Computational Intelligence (WS 2018/19)

Introduction to Computational Intelligence

(Wahlmodul INF-BSc-305 / AR-MSc-306)

Winter term 2018/19

Prof. Dr. Günter Rudolph

Wednesday 10:15 - 11:45 am Campus North, OH12, E.003
First Lecture: Wednesday, 10-OCT-2018


  • 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.

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.

The slides about organizational issues are in the first set of slides (10.10.18) below.
10.10.18 Artificial Neural Networks I  
17.10.18 Artificial Neural Networks II  
24.10.18 Artificial Neural Networks III  
31.10.18 Fuzzy Systems I  
07.11.18 Fuzzy Systems II  
14.11.18 Fuzzy Systems III  
21.11.18 Fuzzy Systems IV  
05.12.18 Evolutionary Algorithms I  
12.12.18 Evolutionary Algorithms II  
19.12.18 Evolutionary Algorithms III  
09.01.19 Evolutionary Algorithms IV  
16.01.19 Swarm Intelligence  

  • 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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