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{{CourseHeader
 
{{CourseHeader
     | code = 2-IKV-115
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     | code = 2-IKV-115a
 
     | title = Introduction to Computational Intelligence
 
     | title = Introduction to Computational Intelligence
 
}}
 
}}
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== News ==
 
== News ==
 
<!--'''Exam:''' The list of questions is [http://dai.fmph.uniba.sk/courses/ICI/ci-exam-questions.pdf here]. You will choose three questions (pseudo)randomly.-->
 
<!--'''Exam:''' The list of questions is [http://dai.fmph.uniba.sk/courses/ICI/ci-exam-questions.pdf here]. You will choose three questions (pseudo)randomly.-->
<!--
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;16. 9. 2018
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: We start on Monday, 24th September.
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<!-- [[#Archív noviniek|Archív noviniek…]] -->
 
<!-- [[#Archív noviniek|Archív noviniek…]] -->
 
<!--b>Poznámka:</b> Aktualizované slajdy prednášok budú poskytované priebežne na stránke predmetu.-->
 
  
 
== Course schedule ==
 
== Course schedule ==
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|-
 
|-
 
|Lecture
 
|Lecture
|Monday
+
|Tuesday
|9:00 - 10:30
+
|8:10 - 9:40
|I-9
+
|I-9 / hybrid
 
|[[Igor Farkas|Igor Farkaš]]
 
|[[Igor Farkas|Igor Farkaš]]
 
|-
 
|-
 
|Seminar
 
|Seminar
 
|Thursday
 
|Thursday
|14:00 - 15:30
+
|13:10 - 14:40
|I-9
+
|i-9 / hybrid
|[[Igor Farkas|Igor Farkaš]] & [[Endre Hamerlik]]  
+
|[[Kristina Malinovska|Kristína Malinovská]]  
 
|}
 
|}
 
  
 
== Syllabus ==
 
== Syllabus ==
  
 
{| class="alternative table-responsive"
 
{| class="alternative table-responsive"
 +
!#
 
!Date
 
!Date
 
!Topic
 
!Topic
 
!References
 
!References
 
|-
 
|-
|23.09.
+
|1.
 +
|21.09.  
 
|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.
+
|2.
 +
|28.09.
 
|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.
+
|3.
 +
|05.10.
 
|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 (2015), ch.12 <!--[http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ DT visualization], [https://www.youtube.com/watch?v=LDRbO9a6XPU DT in python], [https://www.youtube.com/watch?v=2s3aJfRr9gE information entropy], [https://www.youtube.com/watch?v=EuBBz3bI-aA bias-variance tradeoff]-->
 
|-
 
|-
|14.10.
+
|4.
|Supervised learning in feedforward neural networks (perceptrons), pattern classification, function approximation. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-fwdnn.4x.pdf slides]  
+
|12.10.
|R&N (2010), ch.18.2; Marsland (2009), ch.2-3, Engelbrecht (2007), ch.2-3
+
|Supervised learning in feedforward neural networks (perceptrons), pattern classification, regression.   [http://dai.fmph.uniba.sk/courses/ICI/References/ci-fwdnn.4x.pdf slides]
 +
|R&N (2010), ch.18.2; Marsland (2015), ch.3-4, Engelbrecht (2007), ch.2-3
 
|-
 
|-
|21.10.
+
|5.
|Unsupervised (self-organizing) neural networks: feature extraction, data visualization.  [http://dai.fmph.uniba.sk/courses/ICI/References/ci-unsup.4x.pdf slides]  
+
|19.10.
|Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4
+
|Unsupervised (self-organizing) neural networks: feature extraction, data visualization.  [http://dai.fmph.uniba.sk/courses/ICI/References/ci-unsup.4x.pdf slides]
 +
|Marsland (2015), ch.14, Engelbrecht (2007), ch.4
 
|-
 
|-
|28.10.
+
|6.
|Statistical learning, probabilistic models. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-prob.4x.pdf slides]-->
+
|26.10.
|R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2
+
|Probability theory. Bayes formula. Naive Bayes classifier.<!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-prob.4x.pdf slides] -->
 +
|R&N (2010), ch.13,20.1-2
 
|-
 
|-
|04.11.
+
|7.
|Interim summary (review)
+
|02.11.
 +
|Probabilistic learning: ML, MAP.
 
|Thursday: mid-term test
 
|Thursday: mid-term test
 
|-
 
|-
|11.11.
+
|8.
 +
|09.11.
 
|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]-->
 
|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.  
 
|-
 
|-
|18.11.
+
|9.
 +
|16.11.
 
|Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains.
 
|Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains.
 
|R&N (2010), ch.21.3-5; Woergoetter & Porr (2008).
 
|R&N (2010), ch.21.3-5; Woergoetter & Porr (2008).
 
|-
 
|-
|25.11.
+
|10.
 +
|23.11.
 
|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.
+
|11.
 +
|30.11.
 
|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)  
 +
|-
 +
|12.
 +
|07.12.
 +
|Explainable artificial intelligence (XAI). <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-xai.4x.pdf slides]-->
 +
|Barreto Arrieta A. et al. (2020)
 +
|-
 +
|13.
 +
|15.12.
 +
|Summary, recap of main concepts, synergies.
 +
|
 
|}
 
|}
Note: Dates refer to lectures, seminars will be on day+3 each week.
+
Note: Dates refer to lectures, seminars will be on day+2 each week.
  
 
== References ==
 
== References ==
  
 +
* Barreto Arrieta A. et al. (2020). [https://doi.org/10.1016/j.inffus.2019.12.012 Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI]. Information Fusion, 58, pp. 82-115.
 
* Craenen B., Eiben A. (2003): [http://dai.fmph.uniba.sk/courses/ICI/craenen.ci.enc03.pdf Computational Intelligence]. In: Encyclopedia of Life Support Sciences, EOLSS Publishers Co.
 
* Craenen B., Eiben A. (2003): [http://dai.fmph.uniba.sk/courses/ICI/craenen.ci.enc03.pdf Computational Intelligence]. In: Encyclopedia of Life Support Sciences, EOLSS Publishers Co.
 
* Engelbrecht A. (2007). [http://dai.fmph.uniba.sk/courses/ICI/engelbrecht.comp-intel-intro.07.pdf Computational Intelligence: An Introduction] (2nd ed.), John Willey & Sons.
 
* Engelbrecht A. (2007). [http://dai.fmph.uniba.sk/courses/ICI/engelbrecht.comp-intel-intro.07.pdf Computational Intelligence: An Introduction] (2nd ed.), John Willey & Sons.
Line 101: Line 119:
 
* Sloman A. (2002). [http://www.cs.bham.ac.uk/research/projects/cogaff/sloman.turing.irrelevant.pdf The Irrelevance of Turing Machines to AI]. In Scheutz M. (ed.): Computationalism: New Directions, MIT Press, Cambridge, MA, pp. 87–127.
 
* Sloman A. (2002). [http://www.cs.bham.ac.uk/research/projects/cogaff/sloman.turing.irrelevant.pdf The Irrelevance of Turing Machines to AI]. In Scheutz M. (ed.): Computationalism: New Directions, MIT Press, Cambridge, MA, pp. 87–127.
 
* Woergoetter F., Porr B. (2008). [http://www.scholarpedia.org/article/Reinforcement_learning Reinforcement learning], Scholarpedia, 3(3):1448.
 
* Woergoetter F., Porr B. (2008). [http://www.scholarpedia.org/article/Reinforcement_learning Reinforcement learning], Scholarpedia, 3(3):1448.
* Zadeh L. (2007). [http://www.scholarpedia.org/article/Fuzzy_logic Fuzzy logic], Scholarpedia, 3(3):1766.  
+
* Zadeh L. (2007). [http://www.scholarpedia.org/article/Fuzzy_logic Fuzzy logic], Scholarpedia, 3(3):1766.
  
 
== Course grading ==
 
== Course grading ==
  
* Active participation during the seminar/exercise: 5 for lectures, 10 for exercises (max. 15 points).  
+
* Active participation during the lectures/exercises (35%): 15 for lectures, 20 for exercises. Minimum 1/3 of points required.
* Written mid-term test (max. 15 points).
+
* Written mid-term test (30%), covering topics of the first half of the semester.
* Final written-oral exam (max. 20 points, 4 questions). Minimum of 7 points required.
+
* Final written-oral exam (30%): We will discuss 3 randomly chosen (by a computer) questions that basically correspond to weekly topics during the semester. Minimum of 1/3 of all points required.
* Optional: small final project (max. 5 points) = implementation of a small neural network (using an existing Python library) and writing a short report. Note: even without this, the student can still get maximum points if s/he has performed very actively.
+
* Small final project (10%) = implementation of a small neural network (using an existing Python library) and writing a short report. Note: even without this, the student can still get maximum points if s/he has performed very actively. Deadline: TBA.
* <b>Overall grading:</b> A (50-46), B (45-41), C (40-36), D (35-31), E (30-26), Fx (25-0).
+
* <b>Overall grading:</b> A (>90%), B (>80%), C (>70%), D (>60%), E (>50%), Fx (otherwise).

Latest revision as of 07:30, 19 October 2021

Introduction to Computational Intelligence 2-IKV-115a

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 Tuesday 8:10 - 9:40 I-9 / hybrid Igor Farkaš
Seminar Thursday 13:10 - 14:40 i-9 / hybrid Kristína Malinovská

Syllabus

# Date Topic References
1. 21.09. What is computational intelligence, basic concepts, relation to artificial intelligence. slides Craenen & Eiben (2003); wikipedia; R&N (2010), chap.1; Sloman (2002)
2. 28.09. Taxonomy of artificial agents, nature of environments. slides R&N (2010), chap.2
3. 05.10. Inductive learning via observations, decision trees. Model selection. slides R&N (2010), ch.18.1-3,18.6; Marsland (2015), ch.12
4. 12.10. Supervised learning in feedforward neural networks (perceptrons), pattern classification, regression. slides R&N (2010), ch.18.2; Marsland (2015), ch.3-4, Engelbrecht (2007), ch.2-3
5. 19.10. Unsupervised (self-organizing) neural networks: feature extraction, data visualization. slides Marsland (2015), ch.14, Engelbrecht (2007), ch.4
6. 26.10. Probability theory. Bayes formula. Naive Bayes classifier. R&N (2010), ch.13,20.1-2
7. 02.11. Probabilistic learning: ML, MAP. Thursday: mid-term test
8. 09.11. Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. R&N (2010), ch.21.1-2.
9. 16.11. Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains. R&N (2010), ch.21.3-5; Woergoetter & Porr (2008).
10. 23.11. Evolutionary computation: basic concepts, genetic algorithms. Engelbrecht (2007), ch.8
11. 30.11. Fuzzy systems, fuzzy logic and reasoning. Engelbrecht (2007), ch.20-21; Zadeh (2007)
12. 07.12. Explainable artificial intelligence (XAI). Barreto Arrieta A. et al. (2020)
13. 15.12. Summary, recap of main concepts, synergies.

Note: Dates refer to lectures, seminars will be on day+2 each week.

References

Course grading

  • Active participation during the lectures/exercises (35%): 15 for lectures, 20 for exercises. Minimum 1/3 of points required.
  • Written mid-term test (30%), covering topics of the first half of the semester.
  • Final written-oral exam (30%): We will discuss 3 randomly chosen (by a computer) questions that basically correspond to weekly topics during the semester. Minimum of 1/3 of all points required.
  • Small final project (10%) = implementation of a small neural network (using an existing Python library) and writing a short report. Note: even without this, the student can still get maximum points if s/he has performed very actively. Deadline: TBA.
  • Overall grading: A (>90%), B (>80%), C (>70%), D (>60%), E (>50%), Fx (otherwise).