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Günter Rudolph: Lecture Introduction to Computational Intelligence (WS 2019/20)

Introduction to Computational Intelligence

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

Winter term 2019/20

Prof. Dr. Günter Rudolph



Dates:    
Wednesday 10:15 - 11:45 am Campus North, OH12, E.003
First Lecture: Wednesday, 09-Oct-2019



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:
1st Exam Date: Thursday, 06-Feb-2020, 8:15am - 9:45am.

Registration for students of Informatics, Statistics or Automation & Robotics via BOSS.
Students of other programs please contact the lecturer by email:
Please include your full name, your matriculation number and degree program!
Please use your unimail account for email communication with the university!

Registration Deadline: 1 week prior to exam

De-registration via BOSS and email, respectively. According to current official regulations you may de-register until 1 day prior to the exam!

Notes regarding registration:
  • Registration is possible although course achievement (Studienleistung) does not exist or is not stored in BOSS. Verification takes place after the registration deadline. If not eligible for the exam, we will notify you.

2nd Exam Date: Wednesday, 25-Mar-2020, 8:15am - 9:45am.


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, 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.


Slides:
Introduction  
09.10.19 Fuzzy Systems I  
16.10.19 Fuzzy Systems II  
23.10.19 Fuzzy Systems III  
30.10.19 Fuzzy Systems IV   (added new slide #34)
06.11.19 Fuzzy Systems V  
13.11.19 Evolutionary Algorithms I  
20.11.19 Evolutionary Algorithms II  
27.11.19 Evolutionary Algorithms III  
04.12.19 Evolutionary Algorithms IV  
11.12.19 Neural Networks I  
18.12.19 Neural Networks II  
08.01.20 Neural Networks III  
15.01.20 Neural Networks IV  

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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