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Günter Rudolph: Lecture Introduction to Computational Intelligence (WS 2015/16)

Introduction to Computational Intelligence

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

Winter term 2015/16

Prof. Dr. Günter Rudolph

Wednesday 10:15 - 11:45 am Campus North, OH12, E.003
First Lecture: Wednesday, 21-OCT-2015


Results of 2nd written exam: download (version: 28-MAR-2016); Grading schema: download
Post-exam review: Wednesday 13th April 2016, 11am - 12noon, Room: OH14, R. 202.

Results of 1st written exam: download (final version: 18-MAR-2016); Grading schema: download
Post-exam review: Friday, 18th March 2016, 2pm - 3pm, Room: OH14, R. 202.

Examination Dates:
1st examination date: Thursday 18-Feb-2016, 12:15h - 13:45h, OH 14, E 23.
2nd examination date: Thursday 24-Mar-2016, 11:15h - 12:45h, 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: 1 week prior to exam.
Note: According to new Hochschulgesetz (University & College Act) in Northrhine-Westphalia you may de-register until 1 day prior to the exam!

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.

Program  TypeRequirements
Informatics,Diplom: Leistungsnachweis  -> must pass tutorial
Informatics,Diplom: Fachprüfung-> written examination (90 min)
Informatics,Bachelor: Module-> written examination (90 min)
Automation & Robotics,Master: Module-> written examination (90 min)
others,Master/Bachelor: Module-> written examination (90 min)

Results of written exam: download

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.

21.10.15 Artificial Neural Networks I  
28.10.15 Artificial Neural Networks II  
04.11.15 Artificial Neural Networks III   updated 11.11.15
11.11.15 Artificial Neural Networks IV  
18.11.15 Fuzzy Systems I  
25.11.15 Fuzzy Systems II  
02.12.15 Fuzzy Systems III  
09.12.15 Fuzzy Systems IV  
06.01.16 Evolutionary Algorithms I  
20.01.16 Evolutionary Algorithms II  
27.01.16 Evolutionary Algorithms III  
27.01.16 Evolutionary Algorithms IV  
10.02.16 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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