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Lecturer(s)
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Petr Pavel, doc. Ing. Ph.D.
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Course content
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unspecified
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Learning activities and teaching methods
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Monological explanation (lecture, presentation,briefing), Self-study (text study, reading, problematic tasks, practical tasks, experiments, research, written assignments)
- Class attendance
- 280 hours per semester
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Learning outcomes
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The aim of the course is to make students familiar with new trends, theoretical knowledge and practical experience in the areas of data mining. Simultaneously this knowledge will enable them to effectively use the presented method in solving practical problems in their study and work.
Students obtain knowledge in given course in accordance with requirements and course programme.
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Prerequisites
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Unspecified
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Assessment methods and criteria
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Oral exam, Essay, Student's performance analysis
Essay and its presentation
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Recommended literature
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Feldman, R., Sanger, J. The text mining handbook : advanced approaches in analyzing unstructured data.. Cambridge University Press, Cambridge, 2007.
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Charu C. Aggarwal, Ch. C., Zhai, Ch. Mining text data.. Springer-Verlag Company, New York, 2012.
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Miner, G. a kol. Practical text mining and statistical analysis for non-structured text data applications.. Waltham Academic Press, 2012.
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Tufféry, S. Data mining and statistics for decision making.. John Wiley & Sons, Chichester, 2011.
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Witten, I. H. Data mining - practical machine learning tools and techniques.. Morgan Kaufmann, Burlington, 2011.
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