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|24.10. Wed!
|24.10. Wed!
|Unsupervised (self-organizing) neural networks: feature extraction, data visualization. <!--[ slides]-->
|Unsupervised (self-organizing) neural networks: feature extraction, data visualization. [ slides]  
|Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4
|Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4

Revision as of 16:43, 24 October 2018

Introduction to Computational Intelligence 2-IKV-115

The course objectives are to make the students familiar with basic principles of various computational methods of data processing that can commonly be called computational intelligence (CI). This includes mainly bottom-up approaches to solutions of (hard) problems based on various heuristics (soft computing), rather than exact approaches of traditional artificial intelligence based on logic (hard computing). Examples of CI are nature-inspired methods (artificial neural networks, evolutionary algorithms, fuzzy systems), as well as probabilistic methods and reinforcement learning. After the course the students will be able to conceptually understand the important terms and algorithms of CI, and choose appropriate method(s) for a given task. The theoretical lectures are combined with the seminar where the important concepts will be discussed and practical examples will be shown.

Course schedule

Type Day Time Room Lecturer
Lecture Monday 9:00 - 10:30 I-9 Igor Farkaš
Seminar Wednesday 14:00 - 15:30 I-9 Igor Farkaš & Xenia Daniela Poslon


Date Topic References
24.09. What is computational intelligence, basic concepts, relation to artificial intelligence. slides Craenen & Eiben (2003); wikipedia; R&N (2010), chap.1; Sloman (2002)
01.10. Taxonomy of artificial agents, nature of environments. slides R&N (2010), chap.2
08.10. Inductive learning via observations, decision trees. Model selection. slides R&N (2010), ch.18.1-3,18.6; Marsland (2009), ch.6.1-2, visualization, interactive demo
15.10. / 18.10. Supervised learning in feedforward neural networks (perceptrons), pattern classification, function approximation. slides R&N (2010), ch.18.2; Marsland (2009), ch.2-3, Engelbrecht (2007), ch.2-3
24.10. Wed! Unsupervised (self-organizing) neural networks: feature extraction, data visualization. slides Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4
29.10. No class fall break (the whole week)
05.11. Interim summary (review) Wednesday: mid-term test
12.11. Statistical learning, probabilistic models. R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2
19.11. Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. R&N (2010), ch.21.1-2.
26.11. Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains. R&N (2010), ch.21.3-5; Woergoetter & Porr (2008).
03.11. Evolutionary computation: basic concepts, genetic algorithms. Engelbrecht (2007), ch.8
10.12. Fuzzy systems, fuzzy logic and reasoning. Engelbrecht (2007), ch.20-21; Zadeh (2007)


Course grading

  • Active participation during the seminar/exercise: 5 for lectures, 10 for exercises (max. 15 points)
  • Written mid-term test (max. 10 points).
  • Final project: You will implement a small neural network, test it and write a short report (max. 10 points).
  • Final written-oral exam (max. 15 points, 3 questions). Minimum of 5 points required.
  • Overall grading: A (50-46), B (45-41), C (40-36), D (35-31), E (30-26), Fx (25-0).