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Students will understand methods and techniques of computational intelligence that are mostly nature-inspired, parallel by nature, and applicable to many problems. They will learn how these methods work and how to apply them to problems related to data mining, control, intelligen games, optimizations, etc.
Last update: Jirát Jiří (10.01.2014)
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Students will be able to: Understand basic methods and techniques of computational intelligence that stem from the classical artificial intelligence Apply them in knowledge engineering. Last update: Jirát Jiří (31.01.2014)
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R:Konar, A. ''Computational Intelligence: Principles, Techniques and Applications''. Springer, 2005. ISBN 3540208984. R:Bishop, C. M. ''Neural Networks for Pattern Recognition''. Oxford University Press, 1996. ISBN 0198538642. Last update: Jirát Jiří (10.01.2014)
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1. Introduction to computational intelligence, its uses. 2. Algorithms of machine learning. 3. Neural networks. 4. Evolutionary algorithms, evolution of neural networks. 5. [3] Computational intelligence methods: for clustering, for classification, for modeling and prediction. 6. Fuzzy logic. 7. Swarms (PSO, ACO). 8. Ensemble methods. 9. Inductive modeling. 10. Quantum and DNA computing. 11. Case studies, new trends. Last update: Jirát Jiří (10.01.2014)
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https://edux.fit.cvut.cz/courses/MI-MVI/ (login necessary) Last update: Jirát Jiří (10.01.2014)
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none Last update: Jirát Jiří (31.01.2014)
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| Teaching methods | ||||
| Activity | Credits | Hours | ||
| Účast na přednáškách | 1 | 28 | ||
| Práce na individuálním projektu | 2.2 | 61 | ||
| Účast na seminářích | 0.5 | 14 | ||
| 4 / 4 | 103 / 112 | |||