460-4126/01 – Methods of Vector Data Analysis (MAVD)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantorprof. Ing. Jan Platoš, Ph.D.Subject version guarantorprof. Ing. Jan Platoš, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Study languageCzech
Year of introduction2019/2020Year of cancellation2021/2022
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
PLA06 prof. Ing. Jan Platoš, Ph.D.
PRO0199 Ing. Petr Prokop
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 2+2

Subject aims expressed by acquired skills and competences

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.

Teaching methods



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.

Compulsory literature:

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 literature:

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]

Way of continuous check of knowledge in the course of semester

Students' knowledge is verified through the implementation of tasks in exercises, elaboration of data analysis and implementation of some of the discussed methods in the framework of individual work.


Other requirements

Additional requirements are not placed on the student.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

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

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester, validity until: 2021/2022 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Graded credit Graded credit 100  51 3
Mandatory attendence participation: Completion of the task defined by teacher.

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Conditions for subject completion and attendance at the exercises within ISP:

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (N0688A140014) Industry 4.0 AZD P Czech Ostrava 1 Compulsory study plan
2020/2021 (N0688A140014) Industry 4.0 AZD P Czech Ostrava 1 Compulsory study plan
2019/2020 (N0688A140014) Industry 4.0 AZD P Czech Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

2021/2022 Winter
2020/2021 Winter