# Introduction to Computational Intelligence 2-IKV-115a

## Contents

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. slides | R&N (2010), ch.13,20.1-2 |

7. | 02.11. | Probabilistic learning: MAP, ML. | Thursday: mid-term test |

8. | 09.11. | Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. slides | 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. | Reinforcement learning + revision | |

11. | 30.11. | Evolutionary computation: basic concepts, genetic algorithms. slides | Engelbrecht (2007), ch.8 |

12. | 07.12. | Fuzzy systems, fuzzy logic and reasoning. slides | Engelbrecht (2007), ch.20-21; Zadeh (2007) |

13. | 14.12. | Explainable artificial intelligence (XAI) + Revision of main concepts. slides | Barreto Arrieta A. et al. (2020) |

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

## References

- Barreto Arrieta A. et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, pp. 82-115.
- Craenen B., Eiben A. (2003): Computational Intelligence. In: Encyclopedia of Life Support Sciences, EOLSS Publishers Co.
- Engelbrecht A. (2007). Computational Intelligence: An Introduction (2nd ed.), John Willey & Sons.
- Russell S., Norwig P. (2010). Artificial Intelligence: A Modern Approach, (3rd ed.), Prentice Hall. Available in the faculty library.
- Marsland S. (2015). Machine Learning: An Algorithmic Perspective, (2nd ed.), CRC Press.
- Sloman A. (2002). 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). Reinforcement learning, Scholarpedia, 3(3):1448.
- Zadeh L. (2007). Fuzzy logic, Scholarpedia, 3(3):1766.

## 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: 31.1.2022.** -
**Overall grading:**A (>90%), B (>80%), C (>70%), D (>60%), E (>50%), Fx (otherwise).