Terminated in academic year 2021/2022

Methods of Vector Data Analysis

Type of study	Follow-up Master
Language of instruction	Czech
Code	460-4126/01
Abbreviation	MAVD
Course title	Methods of Vector Data Analysis
Credits	4
Coordinating department	Department of Computer Science
Course coordinator	prof. Ing. Jan Platoš, Ph.D.

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

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, .

Advised literature

Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University
Press, May 2014. .
Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press,
Novemeber 2014, , On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12]

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