| Course Unit Code | 460-4102/01 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Choice-compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | Second Year |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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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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| PLA06 | prof. Ing. Jan Platoš, Ph.D. |
| Summary |
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| Evaluation and interpretation of information obtained from the measured and recorded data from the practice. |
| Learning Outcomes of the Course Unit |
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Graduate Course gives the following knowledge and skills:
basic theoretical background for data analysis,
implementation and application of selected methods,
practical application to real data,
application of selected software packed to data analysis,
visualization and analysis results.
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| Course Contents |
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Machine learning
Bayesian networks
Reinforcement learning
Bagging
Boosting
Stacking
complex network
Ranking
Analysis of tensor data
Graph databases
Data visualization |
| Recommended or Required Reading |
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| Required Reading: |
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Han Jiawei; Kamber Micheline; Pei Jian, Data Mining, The Morgan Kaufmann Series in Data Management Systems, 3rd edition, 2011.
Mohammed J. Zaki, Wagner Meira. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014
Langville, Amy N.; Meyer, Carl D. D. Who's #1?: The Science of Rating and Ranking. Princeton University Press. 2012.
Robinson, Ian; Webber, Jim; Eifrem. Graph Databases. O'Reilly Media. 2013.
Murphy, Kevin P. Machine Learning: A Probabilistic Perspective.The MIT Press. 2013.
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H. Řezanková, D. Húsek, V. Snášel, Shluková analýza dat Druhé rozšířené vydání, Professional Publishing, 2009.
Han Jiawei; Kamber Micheline; Pei Jian, Data Mining, The Morgan Kaufmann Series in Data Management Systems, 3rd edition, 2011.
Mohammed J. Zaki, Wagner Meira. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014
Langville, Amy N.; Meyer, Carl D. D. Who's #1?: The Science of Rating and Ranking. Princeton University Press. 2012.
Robinson, Ian; Webber, Jim; Eifrem. Graph Databases. O'Reilly Media. 2013.
Murphy, Kevin P. Machine Learning: A Probabilistic Perspective.The MIT Press. 2013.
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| Recommended Reading: |
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| D. Skillicorn, Understanding Complex datasets: data mining with matrix decompositions, Chapman & Hall/CRC, 2007. |
| D. Skillicorn, Understanding Complex datasets: data mining with matrix decompositions, Chapman & Hall/CRC, 2007. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Graded credit | Graded credit | 100 | 51 |