(Syllabus)
(Course schedule)
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|Tuesday
 
|Tuesday
 
|8:10 - 9:40
 
|8:10 - 9:40
|online
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|I-9 / hybrid
 
|[[Igor Farkas|Igor Farkaš]]
 
|[[Igor Farkas|Igor Farkaš]]
 
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|Thursday
 
|Thursday
 
|13:10 - 14:40
 
|13:10 - 14:40
|online
+
|i-9 / hybrid
 
|[[Kristina Malinovska|Kristína Malinovská]]  
 
|[[Kristina Malinovska|Kristína Malinovská]]  
 
|}
 
|}

Revision as of 14:46, 20 September 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. Craenen & Eiben (2003); wikipedia; R&N (2010), chap.1; Sloman (2002)
2. 28.09. Taxonomy of artificial agents, nature of environments. R&N (2010), chap.2
3. 05.10. Inductive learning via observations, decision trees. Model selection. 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, function approximation. 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. 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+3 each week.

References

Course grading

  • Active participation during the lectures/exercises (35%): 15 for lectures, 20 for exercises.
  • Written mid-term test (30%).
  • Final oral exam (30%): you will choose 3 questions, minimum of 1/3 of all points required. We will discuss 3 randomly chosen (by a computer) questions that basically correspond to weekly topics during the semester.
  • 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: 17th January, 2021.
  • Overall grading: A (>90%), B (>80%), C (>70%), D (>60%), E (>50%), Fx (otherwise).