Introduction to Computational Intelligence

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

Winter term 2022/23

Prof. Dr. Günter Rudolph



Dates:    
Wednesday 10:15 - 11:45 am in lecture hall E23 (OH14)
First Lecture: Wednesday, 12-Oct-2022

Link to Moodle


News:


Tutorial: Marius Bommert (M.Sc.), Alexander van der Staay (B.Sc.), Gero Grühn (cand. 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  
12.10.2022 Fuzzy Systems I  
19.10.2022 Fuzzy Systems II  
26.10.2022 Fuzzy Systems III  
02.11.2022 Fuzzy Systems IV  
09.11.2022 Evolutionary Algorithms I  
16.11.2022 Evolutionary Algorithms II  
23.11.2022 Evolutionary Algorithms III  
30.11.2022 Evolutionary Algorithms IV  
07.12.2022 Swarm Intelligence  
14.12.2022 Neural Networks I  
21.12.2022 Neural Networks II  
11.01.2023 Neural Networks III   (slide 17 corrected)
18.01.2023 Neural Networks IV  
25.01.2023 Neural Networks V  
01.02.2023 Fuzzy Systems V  

Literature: