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Günter Rudolph: Lecture Introduction to Computational Intelligence (WS 2014/15)

Introduction to Computational Intelligence

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

Winter term 2014/15

Prof. Dr. Günter Rudolph

Wednesday 10:15 - 11:45 am Campus North, OH12, E.003
First Lecture: Wednesday, 06-OCT-2014

Tutorials: Simon Wessing, 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)

1st Examination date: Mon, 02-MAR-2015, 10:30-12:00am in room HGII / HS6.
Results of written exam: download

Post-exam review: Friday, 17th April 2015, 1:00-2:00pm , OH 14, Room 202.

2nd Examination date: Wed, 30-SEP-2015, 02:15-03:45pm in room HG II / HS2.
Results of written exam: download

Post-exam review: Tuesday, 15th December 2015, 3:00-4:00pm , OH 14, Room 203.

Registration Online:
ProgramRegistration via
Master Automation & RoboticsBOSS
Bachelor InformatikBOSS
Bachelor Angewandte InformatikBOSS
Diplom InformatikBOSS
Diplom Angewandte InformatikBOSS
Master Statistik etc.Dekanat Statistik

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.

08.10.14 Artificial Neural Networks I  
15.10.14 Artificial Neural Networks II  
22.10.14 Artificial Neural Networks III  
29.10.14 Artificial Neural Networks IV  
12.11.14 Fuzzy Systems I  
19.11.14 Fuzzy Systems II  
26.11.14 Fuzzy Systems III  
17.12.14 Fuzzy Systems IV  
07.01.15 Evolutionary Algorithms I  
14.01.15 Evolutionary Algorithms II  
29.01.14 Evolutionary Algorithms III  
21.01.15 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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