Terminated in academic year 2022/2023

Data Analysis III

Type of study	Follow-up Master
Language of instruction	English
Code	460-4099/02
Abbreviation	MAD III
Course title	Data Analysis III
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

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

Advised literature

1. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. .
2. 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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