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Lecturer(s)
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Salavec Tomáš
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Hájek Petr, prof. Ing. Ph.D.
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Asante Andrew
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Course content
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unspecified
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Laboratory work
- Individual project
- 40 hours per semester
- Contact teaching
- 28 hours per semester
- Preparation for an exam
- 30 hours per semester
- Home preparation for classes
- 52 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, Discussion
Assignment: completion and successful defence of tasks and a project with a success rate of at least 60 %. The examination is written with a success rate of at least 60 %.
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Recommended literature
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BERKA, P. Dobývání znalostí z databází. Praha, 2003.
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KONONENKO, I., KUKAR, M. Machine learning and data mining. Amsterdam, 2007.
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PYLE, D. Business modeling and data mining. San Francisco, 2003.
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PYLE, D. Data preparation for data mining. San Francisco, 1999.
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SHMUELI, G. Data mining for business analytics. Hoboken, 2016.
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WITTEN, I. H. Data mining. San Francisco, 2005.
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