460-6026/02 – Machine Learning (SU)

Gurantor departmentDepartment of Computer ScienceCredits10
Subject guarantordoc. Ing. Jan Platoš, Ph.D.Subject version guarantordoc. Ing. Jan Platoš, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
PLA06 doc. Ing. Jan Platoš, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Part-time Examination 28+0

Subject aims expressed by acquired skills and competences

The aim of the course is to provide students a detail overview of methodology and methods of machine learning. In addition, this knowledge and skills will be further enhanced in a direction that is in line with the specific focus of its Ph.D. studies and dissertation work.

Teaching methods

Individual consultations
Project work
Other activities


The subject is focused on the data processing and data analysis with respect to knowledge mining. The subject covers all states of data processing from its acquisition, preprocessing and cleaning to classification or clustering and visualization. The main focus will be targeted to the processing of medical and laboratory data but other data sources will be discussed as well.

Compulsory 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.

Recommended 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.

Way of continuous check of knowledge in the course of semester

Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.


Other requirements

The student prepares and presents the work on a given topic.


Subject has no prerequisities.


Subject has no co-requisities.

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

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (P0588D140004) Bioinformatics and Computational Biology P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0588D140004) Bioinformatics and Computational Biology K English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner