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Günter Rudolph: Lecture Introduction to Computational Intelligence (WS 2022/23)

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:
  • Published 04-Feb-2023 (updated 08-Feb-2023)
    The written exam on Tuesday, 07-Feb-2023 (6:00pm-7:30pm), took place in the lecture halls Ch HS1+2 in the Chemistry building for all study programs. Please come early and wait in the foyer of the lecture hall until we invite you to enter the lecture hall.
    Have student ID and photo ID (passport, ID card, Personalausweis) ready. Without ID you are not allowed to take the exam!
    As always, the exam questions are in English. But you may answer in English or German.
    Neither kind of auxiliary means is allowed. Only use indelible pens with black or blue ink.
    R programming is not required in the exam.
  • Published 25-Jan-2023
    The course achievement (Studienleistung), which is mandatory for registration to the written exam, is now recorded in BOSS. If you still encounter any problems in registering via BOSS, first contact your team in the examination office. Only if they cannot help you, you may contact us directly regarding this matter.
  • Published 20-Jan-2023
    Currently, certain students who wish to register for the exam are denied registration because the course achievement (Studienleistung) is missing. We requested ITMC to switch off this automatic checking in BOSS. If it is still in operation on Wednesday, 25-Jan-2023, we shall publish an alternative registration procedure.
    Important: The deadline for registration is now 7 days prior to the exam.
  • Published 17-Dec-2022 (updated 20-Jan-2013)
    The registration to the exam must be made via BOSS for the study programs BSc (Angew.) Informatik, MSc Automation & Robotics, MSc Data Science, and Msc Statistics. All other study programs must register (exam date, name, matriculation number, study program) by an email sent to Prof. Rudolph. Registration is open now.
    Important: You cannot participate in the exam without due registration. The registration deadline is 7 days prior to the exam. As usual, you may de-register up to one day prior to the exam.
    Notice: Currently, registration is possible without formal eligibility (Studienleistung). But we will scrutinize if you are approved or not. In the end, we'll get you! ;-)
  • Published 30-Nov-2022 (updated 17-Dec-2022)
    Backed by overwhelming majority of votes, the lecture in the last week prior to Christmas (21-Dec-2022) will be presented in online mode via Zoom (no hybrid mode!). The link is available via Moodle.
  • Published 18-Oct-2022 (updated 29-Nov-2022)
    The written exams will take place on
    - Tuesday,07-Feb-2023,6:00pm - 7:30pm.confirmed!
    - Wednesday, 29-Mar-2023,9:00am - 10:30am.confirmed!
    The exams are planned to be run in presence (on-site / in classroom).
    Information about registration will follow in due time.


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:
  • 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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