| Riadok 37: | Riadok 37: | ||
|14:00 | |14:00 | ||
|I-9 | |I-9 | ||
| − | |[[Igor Farkas|Igor Farkaš]] & Xenia Daniela Poslon | + | |[[Igor Farkas|Igor Farkaš]] & [https://www.sav.sk/index.php?lang=en&doc=user-org-user&user_no=11408 Xenia Daniela Poslon] |
|} | |} | ||
| Riadok 50: | Riadok 50: | ||
|24.09. | |24.09. | ||
|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 | + | |Craenen & Eiben (2003); [https://en.wikipedia.org/wiki/Computational_intelligence wikipedia]; R&N (2010), chap.1; Sloman (2002) |
|- | |- | ||
|01.10. | |01.10. | ||
| Riadok 103: | Riadok 103: | ||
* Russell S., Norwig P. (2010). [http://dai.fmph.uniba.sk/courses/ICI/References/russell-norvig.AI-modern-approach.3ed.2010.pdf Artificial Intelligence: A Modern Approach], (3rd ed.), Prentice Hall. Available in the faculty library. | * Russell S., Norwig P. (2010). [http://dai.fmph.uniba.sk/courses/ICI/References/russell-norvig.AI-modern-approach.3ed.2010.pdf Artificial Intelligence: A Modern Approach], (3rd ed.), Prentice Hall. Available in the faculty library. | ||
* Marsland S. (2015). [http://dai.fmph.uniba.sk/courses/ICI/References/marsland.machine-learning.2ed.2015.pdf Machine Learning: An Algorithmic Perspective], (2nd ed.), CRC Press. | * Marsland S. (2015). [http://dai.fmph.uniba.sk/courses/ICI/References/marsland.machine-learning.2ed.2015.pdf Machine Learning: An Algorithmic Perspective], (2nd ed.), CRC Press. | ||
| + | * 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. | ||
Verzia zo dňa a času 17:09, 23. september 2018
Introduction to Computational Intelligence 2-IKV-115
Obsah
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 introduction is combined with practical examples.
Course schedule
| Type | Day | Time | Room | Lecturer |
|---|---|---|---|---|
| Lecture | Monday | 9:00 | I-9 | Igor Farkaš |
| Seminar / Exercise | Wednesday | 14:00 | I-9 | Igor Farkaš & Xenia Daniela Poslon |
Syllabus
| Date | Topic | References |
|---|---|---|
| 24.09. | What is computational intelligence, basic concepts, relation to artificial intelligence. | Craenen & Eiben (2003); wikipedia; R&N (2010), chap.1; Sloman (2002) |
| 01.10. | Taxonomy of artificial agents, nature of environments. | R&N (2010), chap.2 |
| 08.10. | 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 |
| 15.10. | 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 |
| 22.10. | Unsupervised (self-organizing) neural networks: feature extraction, data visualization. | Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4 |
| 29.10. | No class | fall break (the whole week) |
| 05.11. | Interim summary (review) | Wrdnesday: mid-term test |
| 12.11. | Statistical learning, probabilistic models. | R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2 |
| 19.11. | Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. | R&N (2010), ch.21.1-2. |
| 26.11. | Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains. | R&N (2010), ch.21.3-5; Woergoetter & Porr (2008). |
| 03.11. | Evolutionary computation: basic concepts, genetic algorithms. | Engelbrecht (2007), ch.8 |
| 10.12. | Fuzzy systems, fuzzy logic and reasoning. | Engelbrecht (2007), ch.20-21; Zadeh (2007) |
References
- 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. Available in the faculty library.
- 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 seminar/exercise: 5 for lectures, 10 for exercises (max. 15 points)
- Written mid-term test (max. 10 points).
- Final project: You will implement a small neural network, test it and write a short report (max. 10 points).
- Final written-oral exam (max. 15 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).