460-8703/02 – Fundamentals of Machine Learning (ZSU)

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 languageEnglish
Year of introduction2019/2020Year of cancellation2020/2021
Intended for the facultiesFSIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
PLA06 prof. Ing. Jan Platoš, Ph.D.
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 course aims to acquaint students with the data, data analysis, and machine learning at a level that will match absolved subjects and their level of knowledge. The primary knowledge that will be given to students is knowledge about data, their preparation, statistical properties, data processing methods and machine learning. Students will be able to understand the data properties, analytical methods, and will be able to correctly interpret the results achieved and present and visualize these methods.

Teaching methods



The students will learn about the data properties, data storage, and processing in the course. They will also learn methods of data analysis, machine learning, artificial intelligence, interpretation of results and their visualization. The lectures will deal with statistical properties of data, methods of data cleaning and preprocessing. Next, the theoretical description of methods of data processing, machine learning, and artificial intelligence. Students will be able to decide which method is appropriate, what assumptions, what is its principle, and what outputs it can get. The exercises will then serve for practical experiments on suitable datasets, experimenting with data analysis tools, and evaluating results.

Compulsory literature:

Presentation for lectures. HASTIE, Trevor., Robert. TIBSHIRANI and J. H. FRIEDMAN. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer, c2009. ISBN 978-0-387-84858-7. WITTEN, Ian H., Eibe FRANK, Mark A. HALL and Christopher J. PAL. Data mining: Practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, 2017. ISBN 978-0-12-804291-5.

Recommended literature:

LESKOVEC, Jurij, Anand RAJARAMAN and Jeffrey D. ULLMAN. Mining of massive datasets / Jure Leskovec, Standford University, Anand Rajaraman, Milliways Labs, Jeffrey David Ullman, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232. AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1.

Way of continuous check of knowledge in the course of semester

The student knowledge is checked during lab using exercises, home works and implementation of selected algorithm.


Other requirements

Additional requirements are not placed on the student.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

Lectures: 1. Data and their Properties 2. Statistical Data Features 3. Knowledge Representation 4. Exploratory analysis I 5. Exploratory analysis II 6. Basic Algorithms - Clustering 7. Basic Algorithms - Classification/Regression 8. Credibility and Algorithm evaluation 9. Advanced Methods and Algorithms 10. Extending of Linear Model 11. Data Transformation 12. Optimization methods 13. Data Visualization I 14. Data Visualization II Exercises on computer lab: 1. Demonstration of lecture knowledge - data and the properties. 2. Demonstration of lecture knowledge - statistical data proeprties. 3. Demonstration of lecture knowledge - knowledge representations. 4. Demonstration of lecture knowledge - exploratory analysis I 5. Demonstration of lecture knowledge - exploratory analysis II 6. Demonstration of lecture knowledge - custering 7. Demonstration of lecture knowledge - classification 8. Demonstration of lecture knowledge - model quality and its measurement. 9. Demonstration of lecture knowledge - tree based algorithms 10. Demonstration of lecture knowledge - non=linear models. 11. Demonstration of lecture knowledge - data transformation. 12. Demonstration of lecture knowledge - introduction into optimization methods 13. Demonstration of lecture knowledge - data visualization. 14. Demonstration of lecture knowledge - data visualization.

Conditions for subject completion

Conditions for completion are defined only for particular subject version and form of study

Occurrence in study plans

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Occurrence in special blocks

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Assessment of instruction

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