Course Unit Code | 460-4126/01 |
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Number of ECTS Credits Allocated | 4 ECTS credits |
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Type of Course Unit * | Compulsory |
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Level of Course Unit * | Second Cycle |
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Year of Study * | First Year |
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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 | Czech |
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Prerequisites and Co-Requisites | There are no prerequisites or co-requisites for this course unit |
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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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This course is focused on algorithms for data analysis and data visualization. The first part of the course is focused on explorative analysis and data clustering. The second part is focused on the data classification. he course describes a less complex linear methods to the more complex method based on the SVM. More advanced methods will be described in the last part of the course. |
Learning Outcomes of the Course Unit |
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The goal of this course is to deepen and improve the knowledge about data analysis methods acquired in the previous courses. The main information delivered to the students is advanced algorithms for data classification, stream data processing, advanced data structures and machine learning techniques. The students will be able to use these methods, to interpret achieved results. Moreover, the student will be able to presents and visualize the results using proper methods. |
Course Contents |
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Exploration data analysis
1. Frequent patterns, Rule based analysis.
2. Representative based clustering, Hierarchical Clustering.
3. Density based clustering, Cluster validation.
4. Self-organizing maps
5. Anomaly detection
Data Classification
6. Linear classification (Linear discriminant analysis, Naive Bayes, Logistics regression)
7. Decision Trees, Random Forests.
8. Support Vector Machine, Kernel based methods
9. Neural networks (Perceptron, Feed forward NN+Back propagation)
10. Regression methods
11. Advanced classification methods
12. Classification validation
Advanced methods
13. Stream dat analysis
14. Vektor data vizualization |
Recommended or Required Reading |
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Required Reading: |
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Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, Morgan Kaufmann, 2011, ISBN: 978-0123748560
Charu C. Aggarwal, Data Mining - The Textbook, Springer, 2015, ISBN: 978-3-319-14141-1. |
Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, Morgan Kaufmann, 2011, ISBN: 978-0123748560
Charu C. Aggarwal, Data Mining - The Textbook, Springer, 2015, ISBN: 978-3-319-14141-1. |
Recommended Reading: |
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Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University
Press, May 2014. ISBN: 9780521766333.
Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press,
Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12] |
Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University
Press, May 2014. ISBN: 9780521766333.
Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press,
Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12] |
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 |