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Lecturer(s)
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Gangur Mikuláš, Doc. RNDr. Ph.D.
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Course content
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The course is intended for doctoral studies. Use of information and knowledge in decision-making processes. The process of data mining, CRISP-DM methodology. Sources of knowledge mining: databases, statistical methods, machine learning. Machine learning methods: decision trees, decision rules, association rules, neural networks, genetic algorithms, Bayesian learning methods. Use of statistical tools and machine learning tools in SW Statistica and SW Mathematica. Methods of constructed models evaluation. Methods of data exploration and data preparation. Overview of systems for mining knowledge from databases. Principles of Decision Support Systems (DSS) Tools for creating decision support systems. Prediction markets, principles and applications in DSS
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Learning activities and teaching methods
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- Contact hours
- 24 hours per semester
- unspecified
- 80 hours per semester
- Individual project (40)
- 60 hours per semester
- Presentation preparation (report in a foreign language) (10-15)
- 20 hours per semester
- Team project (50/number of students)
- 25 hours per semester
- Preparation for comprehensive test (10-40)
- 40 hours per semester
- Attendance on a field trip (number of real hours - maximum 8h/day)
- 11 hours per semester
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| prerequisite |
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| Knowledge |
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| Build basic SQL statements to get the required data. |
| To apply the knowledge gained in the courses KEM/STA, KEM/SZD and KEM/ADM. |
| To use the theory of statistical testing of hypotheses practically. |
| Perform an exploratory analysis of the data and verify its quality. |
| Analyze the dependence of two variables, apply the theory of regression functions of one explanatory variable. |
| Work with covariance and correlation matrices. |
| Work with matrices, know their properties and apply matrix operations, find eigenvalues and eigenvectors. |
| Skills |
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| Work with selected database system to obtain required data. |
| Work with extended list of statistical functions in MS Excel. |
| Work with basic functions of SW Statistica (data retrieval, data modification, basic statistics, graphs). |
| Work with basic functions of Mathematica SW (basics of working with notebook, inserting functions, working with hint, graphical functions). |
| Competences |
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| N/A |
| create thesis in demanded structure use statistical methods on master level search articles in English and Czech |
| learning outcomes |
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| Knowledge |
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| Know selected methods of data processing and gain knowledge. |
| Understand the principles of selected methods, knowledge of the prerequisites of their use. |
| Understand the outputs and knowledge of procedures to perform interpretation. |
| Understand the principles of data mining and understand the criteria for choosing appropriate methods. |
| Skills |
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| Choose the right method for problem analysis. |
| Analyze and verify data quality. |
| Practically use the selected method in the selected SW (Statistica, Mathematica, MS Excel). |
| Test models and their comparison. Interpretation of results based on outputs. Application of results in self-determination. |
| Competences |
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| N/A |
| use advanced statistical and data mining methods apply suitable DSS software on practical problems |
| teaching methods |
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| Knowledge |
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| Lecture supplemented with a discussion |
| Seminar |
| E-learning |
| Self-study of literature |
| One-to-One tutorial |
| Group discussion |
| Individual study |
| Skills |
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| Seminar |
| Lecture with visual aids |
| One-to-One tutorial |
| Group discussion |
| Competences |
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| One-to-One tutorial |
| Seminar |
| Discussion |
| Lecture with visual aids |
| Students' portfolio |
| assessment methods |
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| Knowledge |
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| Oral exam |
| Project |
| Skills demonstration during seminar |
| Skills |
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| Skills demonstration during practicum |
| Project |
| Seminar work |
| Competences |
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| Skills demonstration during practicum |
| Project |
| Seminar work |
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Recommended literature
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Anderson, David Ray. An introduction to management science : quantitative approaches to decision making. Mason : Thomson/South-Western, 2008. ISBN 978-0-324-39980-6.
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Harrington, Joseph Emmett. Games, strategies, and decision making. New York : Worth Publishers, 2009. ISBN 978-0716766308.
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Mitchell, Tom Michael. Machine learning. Boston : McGraw-Hill, 1997. ISBN 0-07-042807-7.
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Nutt, Paul C. Handbook of decision making. 1st pub. Chichester : John Wiley & Sons, 2010. ISBN 978-1-4051-6135-0.
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Rokach, Lior; Maimon, Oded. Data mining with decision trees : theory and applications. Hackensack : World Scientific, 2008. ISBN 978-981-277-171-1.
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Turban, Efraim; Aronson, Jay E.; Liang, Ting-Peng. Decision support systems and intelligent systems. 7th ed. Upper Saddle River : Pearson/Prentice Hall, 2005. ISBN 0-13-046106-7.
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Vaughan Williams, Leighton. Prediction markets : theory and applications. New York : Routledge, 2011. ISBN 978-0-415-57286-6.
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