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== News ==
 
== News ==
  
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|23.09.
 
|23.09.
|What is computational intelligence, basic concepts, relation to artificial intelligence.  [http://dai.fmph.uniba.sk/courses/ICI/References/ci-def.4x.pdf slides]
+
|What is computational intelligence, basic concepts, relation to artificial intelligence.  <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-def.4x.pdf slides]-->
 
|Craenen & Eiben (2003); [https://en.wikipedia.org/wiki/Computational_intelligence wikipedia]; R&N (2010), chap.1; Sloman (2002)
 
|Craenen & Eiben (2003); [https://en.wikipedia.org/wiki/Computational_intelligence wikipedia]; R&N (2010), chap.1; Sloman (2002)
 
|-
 
|-
 
|30.09.
 
|30.09.
|Taxonomy of artificial agents, nature of environments. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-agents.4x.pdf slides]
+
|Taxonomy of artificial agents, nature of environments. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-agents.4x.pdf slides]-->
 
|R&N (2010), chap.2
 
|R&N (2010), chap.2
 
|-
 
|-
 
|07.10.
 
|07.10.
|Inductive learning via observations, decision trees. Model selection. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-learning.4x.pdf slides]
+
|Inductive learning via observations, decision trees. Model selection. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-learning.4x.pdf slides]-->
 
|R&N (2010), ch.18.1-3,18.6; Marsland (2009), ch.6.1-2, [http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ visualization], [http://fiddle.jshell.net/92Jxj/show/light/ interactive demo]
 
|R&N (2010), ch.18.1-3,18.6; Marsland (2009), ch.6.1-2, [http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ visualization], [http://fiddle.jshell.net/92Jxj/show/light/ interactive demo]
 
|-
 
|-
 
|14.10.
 
|14.10.
|Supervised learning in feedforward neural networks (perceptrons), pattern classification, function approximation. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-fwdnn.4x.pdf slides]  
+
|Supervised learning in feedforward neural networks (perceptrons), pattern classification, function approximation. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-fwdnn.4x.pdf slides] -->
 
|R&N (2010), ch.18.2; Marsland (2009), ch.2-3, Engelbrecht (2007), ch.2-3
 
|R&N (2010), ch.18.2; Marsland (2009), ch.2-3, Engelbrecht (2007), ch.2-3
 
|-
 
|-
 
|21.10.
 
|21.10.
|Unsupervised (self-organizing) neural networks: feature extraction, data visualization. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-unsup.4x.pdf slides]  
+
|Unsupervised (self-organizing) neural networks: feature extraction, data visualization. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-unsup.4x.pdf slides] -->
 
|Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4
 
|Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4
 
|-
 
|-
 
|28.10.
 
|28.10.
|Statistical learning, probabilistic models. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-prob.4x.pdf slides]
+
|Statistical learning, probabilistic models. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-prob.4x.pdf slides]-->
 
|R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2
 
|R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2
 
|-
 
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|11.11.
 
|11.11.
|Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem.  [http://dai.fmph.uniba.sk/courses/ICI/References/ci-rl.4x.pdf slides]
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|Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem.  <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-rl.4x.pdf slides]-->
 
|R&N (2010), ch.21.1-2.  
 
|R&N (2010), ch.21.1-2.  
 
|-
 
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|-
 
|25.11.
 
|25.11.
|Evolutionary computation: basic concepts, genetic algorithms. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-evol.4x.pdf slides]
+
|Evolutionary computation: basic concepts, genetic algorithms. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-evol.4x.pdf slides]-->
 
|Engelbrecht (2007), ch.8
 
|Engelbrecht (2007), ch.8
 
|-
 
|-
 
|03.12.
 
|03.12.
|Fuzzy systems, fuzzy logic and reasoning. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-fuzzy.4x.pdf slides]
+
|Fuzzy systems, fuzzy logic and reasoning. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-fuzzy.4x.pdf slides]-->
 
|Engelbrecht (2007), ch.20-21; Zadeh (2007)  
 
|Engelbrecht (2007), ch.20-21; Zadeh (2007)  
 
|}
 
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Revision as of 14:43, 17 September 2019

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 Thursday 14:50 - 16:30 I-9 Igor Farkaš & Endre Hamerlik ]


Syllabus

Date Topic References
23.09. What is computational intelligence, basic concepts, relation to artificial intelligence. Craenen & Eiben (2003); wikipedia; R&N (2010), chap.1; Sloman (2002)
30.09. Taxonomy of artificial agents, nature of environments. R&N (2010), chap.2
07.10. Inductive learning via observations, decision trees. Model selection. R&N (2010), ch.18.1-3,18.6; Marsland (2009), ch.6.1-2, visualization, interactive demo
14.10. Supervised learning in feedforward neural networks (perceptrons), pattern classification, function approximation. R&N (2010), ch.18.2; Marsland (2009), ch.2-3, Engelbrecht (2007), ch.2-3
21.10. Unsupervised (self-organizing) neural networks: feature extraction, data visualization. Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4
28.10. Statistical learning, probabilistic models. R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2
04.11. Interim summary (review) Thursday: mid-term test
11.11. Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. R&N (2010), ch.21.1-2.
18.11. Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains. R&N (2010), ch.21.3-5; Woergoetter & Porr (2008).
25.11. Evolutionary computation: basic concepts, genetic algorithms. Engelbrecht (2007), ch.8
03.12. Fuzzy systems, fuzzy logic and reasoning. Engelbrecht (2007), ch.20-21; Zadeh (2007)

References

Course grading

  • Active participation during the seminar/exercise: 5 for lectures, 10 for exercises (max. 15 points). It includes optional final project (implementation of a small neural network and writing a short report ("optional" means that the student can still get maximum points if s/he has performed very actively).
  • Written mid-term test (max. 15 points).
  • Final written-oral exam (max. 20 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).