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Lecturer(s)
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Hájek Petr, prof. Ing. Ph.D.
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Asante Andrew
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Chrastina Tomáš, Ing.
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Course content
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Laboratory work
- Contact teaching
- 52 hours per semester
- Home preparation for classes
- 28 hours per semester
- Preparation for a credit (assessment)
- 10 hours per semester
- Preparation for an exam
- 30 hours per semester
- Individual project
- 30 hours per semester
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Learning outcomes
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Prerequisites
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unspecified
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Assessment methods and criteria
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Written examination, Home assignment evaluation, Didactic test, Discussion, Class observation
Requirements for assignment: completion and successful defence of two projects with a success rate of at least 60 %, written test throughout the semester with a success rate of at least 60 %. Requirements for exam: written examination with a success rate of at least 60 %.
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Recommended literature
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BERKA, P. Inteligentní systémy. Praha: Oeconomica, 2008. ISBN 80-200-0496-3.
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ENGELBRECHT, A. P. Computational intelligence: An introduction. Chichester: John Wiley & Sons, 2007. ISBN 978-0470035610.
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HYNEK, J. Genetické algoritmy a genetické programování. Praha: Grada, 2008. ISBN 978-80-247-2695-3.
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KRUSE, R. a kol. Computational intelligence: A methodological introduction. London: Springer, 2013. ISBN 978-1-4471-5849-3.
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MAŘÍK, V., ŠTĚPÁNKOVÁ, O., LAŽANSKÝ J. Umělá inteligence 4. Praha: Academia, 2003. ISBN 9788020010445.
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Olej, Vladimír. Úvod do umělé inteligence : moderní přístupy : distanční opora. Pardubice: Univerzita Pardubice, 2010. ISBN 978-80-7395-307-2.
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ZELINKA, I. Evoluční výpočetní techniky: principy a aplikace. Praha: BEN - technická literatura, 2009. ISBN 9788073002183.
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