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Machine Learning

Type of study Doctoral
Language of instruction English
Code 460-6026/02
Abbreviation SU
Course title Machine Learning
Credits 10
Coordinating department Department of Computer Science
Course coordinator prof. Ing. Jan Platoš, Ph.D.

Subject syllabus

• Data – vector, stream, signals, text, networks.
• Data cleaning, dealing with missing values, aggregation.
• Dimension reduction, dimension expansion.
• Explorative data analysis
• Unsupervised learning – frequent pattern mining, clustering, clustering validation
• Anomaly detection
• Supervised learning
- Classification using linear models
- Classification using probabilistic models
- Classification using non-linear models
- Regression models
• Network data analysis
- Network models
- Clustering, relations
- Community detection
• Data visualization

Literature

• BERGERON, Bryan P. Bioinformatics computing. Upper Saddle River, NJ: Prentice Hall/Professional Technical Reference, c2003. ISBN 9780131008250 .
• TSAI, Jeffrey J.-P a Ka-Lok NG. Computational methods with applications in bioinformatics analysis. New Jersey: World Scientific, 2017. ISBN 978-981-3207-97-4 .

Advised literature

• AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1 .
• ZHANG, Yan-Qing a Jagath Chandana RAJAPAKSE. Machine learning in bioinformatics. Hoboken, N.J.: Wiley, c2009. Wiley series on bioinformatics. ISBN 9780470116623 .


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