Course detail
Computational Intelligence
FSI-9VINAcad. year: 2020/2021
Computational Intelligence covers a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which mathematical or traditional modeling can be useless. The course introduces basic approaches and advance methods used in the field. Practical use of the methods is demonstrated on solving simple engineering problems. Students will be given time to practice of own optimization tasks.
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Specification of controlled education, way of implementation and compensation for absences
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Basic literature
Sima,J., Neruda,R.: Theoretical questions of neural networks, MATFYZPRESS, 1996, ISBN 80-85863-18-9 (CS)
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Syllabus
Block 1: Relationship between Computational Intelligence and Artificial Intelligence. Presentation of engineering tasks. Presentation of student tasks.
Block 2: Evolutionary algorithms, optimisation metaheuristics, swarm intelligence (Genetics Algorithms, Grammatical Evolution, Genetic Programming, Ant Colony Optimisation, metaheuristics HC12).
Block 3: Artificial Neural Networks (feedforward neural networks, recurrent neural networks, self-organisation, deep learning)
Block 4: Individual consultations for own tasks.