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 Monday 9:00 - 10:30 online Igor Farkaš
Seminar Thursday 14:00 - 15:30 online Endre Hamerlik & Igor Farkaš


# Date Topic References
1. 22.09. 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, function approximation. 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. Statistical learning, probabilistic models. slides R&N (2010), ch.13,20.1-2
02.11. Q&A - preparation for midterm Thursday: mid-term test
7. 09.11. Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. slides R&N (2010), ch.21.1-2.
8. 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).
9. 23.11. Evolutionary computation: basic concepts, genetic algorithms. slides Engelbrecht (2007), ch.8
10. 30.11. Fuzzy systems, fuzzy logic and reasoning. slides Engelbrecht (2007), ch.20-21; Zadeh (2007)
11. 07.12. Explainable artificial intelligence (XAI). slides Barreto Arrieta A. et al. (2020)
15.12. Summary, recap of main concepts, synergies.

Note: Dates refer to lectures, seminars will be on day+3 each week.


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).
Revision as of 13:26, 11 May 2021 by Farkas (Talk | contribs) (Course grading)