| Course Unit Code | 460-4071/02 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Optional |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | English |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| OH140 | RNDr. Eliška Ochodková, Ph.D. |
| KUD007 | doc. Mgr. Miloš Kudělka, Ph.D. |
| Summary |
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| The course is focused on basic approaches, methods, and algorithms for data mining and network analysis so that it can be applied to the individual work of students in labs. Exercises will provide space for discussion of problems, demonstration of practical tasks and practice on simple assignments. |
| Learning Outcomes of the Course Unit |
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| The course provides basic information about methods used for data mining and network analysis. Students will gain knowledge and skills necessary for further development in this area and the ability to apply them to simple problems. They will be able to assess the applicability of methods for different types of data and evaluate the outcomes of the application of the used methods. |
| Course Contents |
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1. Data for data mining, types and sources of data
2. Attributes and their types, sparse data, incomplete and inaccurate data
3. Algebraic and geometric interpretation of data
4. Probabilistic interpretation of data
5. Numerical and categorial attributes, the basic analytical approaches
6. Data mining, pre-processing and data cleaning
7. Data representation
8. Foundations of data analysis (classification, clustering)
9. Networks and their properties
10. Types of networks and their representation
11. Basic measures and metrics
12. Structure and global properties of networks
13. Basic data structures for network representation
14. Basic algorithms for network analysis |
| Recommended or Required Reading |
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| Required Reading: |
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Presentations of lectures.
Ian H. Witten, Eibe Frank , Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques (Third Edition). The Morgan Kaufmann Series in Data Management Systems, 2011. ISBN 978-0123748560.
Zaki, M. J., Meira Jr, W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.
Mark Newman. Networks: An Introduction. Oxford University Press, 2010. ISBN 978-0199206650. |
Prezentace k přednáškám.
Ian H. Witten, Eibe Frank , Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques (Third Edition). The Morgan Kaufmann Series in Data Management Systems, 2011. ISBN 978-0123748560.
Zaki, M. J., Meira Jr, W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.
Mark Newman. Networks: An Introduction. Oxford University Press, 2010. ISBN 978-0199206650.
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| Recommended Reading: |
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Bramer, M. (2013). Principles of data mining. Springer.
Leskovec, J., Rajaraman, A., Ullman, J. D. (2014). Mining of massive datasets. Cambridge University Press.
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Bramer, M. (2013). Principles of data mining. Springer.
Leskovec, J., Rajaraman, A., Ullman, J. D. (2014). Mining of massive datasets. Cambridge University Press.
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| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Tasks are not Defined |