| Riadok 13: | Riadok 13: | ||
'''Skúška:''' v AISe sú vypísané termíny (7., 9., 15., 20., 26.6), so začiatkom písomnej časti vždy o 8:30 v I-8. Môžete sa prihlasovať, maximálne 8 ľudí na termín. Odhlasovanie (prihlasovanie) je možné do 8:30 (18:30) v deň pred skúškou. Ťaháte si pseudonáhodne 3 otázky zo [http://dai.fmph.uniba.sk/courses/NN/ns-otazky.pdf zoznamu otázok]. | '''Skúška:''' v AISe sú vypísané termíny (7., 9., 15., 20., 26.6), so začiatkom písomnej časti vždy o 8:30 v I-8. Môžete sa prihlasovať, maximálne 8 ľudí na termín. Odhlasovanie (prihlasovanie) je možné do 8:30 (18:30) v deň pred skúškou. Ťaháte si pseudonáhodne 3 otázky zo [http://dai.fmph.uniba.sk/courses/NN/ns-otazky.pdf zoznamu otázok]. | ||
| − | ; | + | ;11. 9. 2018 |
| − | : We start on Monday, | + | : We start on Monday, 24th September. |
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| Riadok 33: | Riadok 33: | ||
|[[Igor Farkas|Igor Farkaš]] | |[[Igor Farkas|Igor Farkaš]] | ||
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| − | | | + | |Seminar / Exercise |
| − | | | + | |Wednesday |
| − | | | + | |14:00 |
|I-9 | |I-9 | ||
| − | |[[ | + | |[[Igor Farkas|Igor Farkaš]] & Xenia Daniela Poslon |
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| Riadok 48: | Riadok 48: | ||
!References | !References | ||
|- | |- | ||
| − | | | + | |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 | ||
|- | |- | ||
| − | | | + | |01.10. |
|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 | ||
|- | |- | ||
| − | | | + | |08.10. |
|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 (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] | ||
|- | |- | ||
| − | | | + | |15.10. |
|Supervised learning in feedforward neural networks (perceptrons), pattern classification, function approximation. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-fwdnn.4x.pdf slides]--> | |Supervised learning in feedforward neural networks (perceptrons), pattern classification, function approximation. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-fwdnn.4x.pdf slides]--> | ||
|R&N (2010), ch.18.2; Marsland (2009), ch.2-3, Engelbrecht (2007), ch.2-3 | |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. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-unsup.4x.pdf slides]--> | |Unsupervised (self-organizing) neural networks: feature extraction, data visualization. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-unsup.4x.pdf slides]--> | ||
|Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4 | |Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4 | ||
|- | |- | ||
| − | | | + | |29.10. |
|mid-term test (special date!) | |mid-term test (special date!) | ||
|your mind :-) | |your mind :-) | ||
|- | |- | ||
| − | | | + | |05.11. |
|Statistical learning, probabilistic models. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-prob.4x.pdf slides]--> | |Statistical learning, probabilistic models. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-prob.4x.pdf slides]--> | ||
|R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2 | |R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2 | ||
|- | |- | ||
| − | | | + | |12.11. |
|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. | ||
|- | |- | ||
| − | | | + | |19.11. |
|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). | ||
|- | |- | ||
| − | | | + | |26.11. |
|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. |
|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) | ||
Verzia zo dňa a času 22:50, 11. 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 |
| 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. | mid-term test (special date!) | your mind :-) |
| 05.11. | Statistical learning, probabilistic models. | R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2 |
| 12.11. | Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. | R&N (2010), ch.21.1-2. |
| 19.11. | Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains. | R&N (2010), ch.21.3-5; Woergoetter & Porr (2008). |
| 26.11. | Evolutionary computation: basic concepts, genetic algorithms. | Engelbrecht (2007), ch.8 |
| 03.12. | 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 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.
- 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 semester (max. 14 points).
- Written mid-term test (max. 12 points).
- Final written-oral exam (max. 24 points, 3 questions).
- Overall grading: A (50-46), B (45-41), C (40-36), D (35-31), E (30-26), Fx (25-0).