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Günter Rudolph: Lecture Introduction to Computational Intelligence (WS 2024/25)

Introduction to Computational Intelligence

(Elective Module INF-BSc-305 / AR-MSc-306)

Winter term 2024/25

Prof. Dr. Günter Rudolph



Dates:    
Wednesday 10:15 - 11:45 am in lecture hall E23 (OH14)
First Lecture: Wednesday, 09-Oct-2024

Link to Moodle


News:
  • 29-Sep-2024
    The module Computational Intelligence consists of a lecture and a tutorial, that is offered twice a week. You will attend only one of the tutorials as they are identical.

    It is not necessary to enroll to the lecture; simply come to the lecture hall E23 in building OH14 at the given date.

    But you should register to the tutorial; simply click on the link to moodle above and you will get further information about this issue.


Tutorial: Dr.-Ing. Marco Pleines, Jonas Kramer (B.Sc.)

Instruction Language: English.


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, RBF nets, Hopfield nets, convolutional neural nets (CNN), recurrent nets.
Foundations of FS: Fuzzy sets, fuzzy numbers, fuzzy logic, fuzzy reasoning, fuzzy control, fuzzy clustering.
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:
Introduction and Organizational Issues  

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.
  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning. MIT Press 2017.
  • 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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