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Günter Rudolph: Lecture Introduction to Computational Intelligence (WS 2016/17)

Introduction to Computational Intelligence

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

Winter term 2016/17

Prof. Dr. Günter Rudolph

Wednesday 10:15 - 11:45 am Campus North, OH12, E.003
First Lecture: Wednesday, 19-OCT-2016


Results of 2nd written exam: download (version: 28-MAR-2017); Grading schema: download
Post-exam review: Monday 24th April 2017, 4pm - 5pm, Room: OH14, R. 202.

Results of 1st written exam: download (final version: 14-MAR-2017); Grading schema: download ; Performance record: download

Post-exam review: Monday, 13th March 2017, 4pm - 5pm, Room: OH14, R. 202.

Examination Dates:
1stexamination date:Friday17-Feb-2017,08:15am - 09:45am, OH 14, E 23.
2ndexamination date:Thursday23-Mar-2017,08:15am - 09:45am, OH 14, E 23.

Registration for students of Informatik or Automation & Robotics via BOSS. Students of other programs please contact me by email.

Registration deadline for 2nd exam:
  • 1 week prior to exam for students of all programs.
Note: According to Hochschulgesetz (University & College Act) in Northrhine-Westphalia you may de-register until 1 day prior to the exam! De-registering via BOSS and email, respectively.

Tutorials: Vanessa Volz (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.

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.

19.10.16 Artificial Neural Networks I  
26.10.16 Artificial Neural Networks II  
02.11.16 Artificial Neural Networks III  
09.11.16 Artificial Neural Networks IV  
16.11.16 Fuzzy Systems I  
23.11.16 Fuzzy Systems II   slides 17 and 20 corrected! (if need be, clear browser cache)
30.11.16 Fuzzy Systems III  
07.12.16 Fuzzy Systems IV  
14.12.16 Fuzzy Systems V  
11.01.17 Evolutionary Algorithms I  
18.01.17 Evolutionary Algorithms II  
25.01.17 Evolutionary Algorithms III  
01.02.17 Evolutionary Algorithms IV  

  • 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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