460-2064/01 – Fundamentals of Machine Learning (ZSU)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantordoc. Ing. Jan Platoš, Ph.D.Subject version guarantordoc. Ing. Jan Platoš, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year3Semesterwinter
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesBachelor
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 Graded credit 2+2
Combined Graded credit 14+0

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

Lectures
Tutorials

Summary

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.

E-learning

Další požadavky na studenta

Additional requirements are not placed on the student.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: 1. Data and their Properties 2. Statistical Data Features 3. Knowledge Representation 4. Basic Algorithms 5. Credibility and Algorithm evaluation 6. Advanced Methods and Algorithms 7. Extending of Linear Model 8. Data Transformation 9. Optimization methods 10. Data Vizualization 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 - linear models. 5. Demonstration of lecture knowledge - model quality and its measurement. 6. Demonstration of lecture knowledge - non=linear models. 7. Demonstration of lecture knowledge - data transformation. 8. Demonstration of lecture knowledge - data transformation. 9. Demonstration of lecture knowledge - optimization method introduction. 10. Demonstration of lecture knowledge - 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
Graded credit Graded credit 100  51
Mandatory attendence parzicipation: Fulfillment of the task from the teacher.

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (B0714A060010) Telecommunication Technology P Czech Ostrava 3 Optional study plan
2019/2020 (B0714A150003) Computer Systems for the Industry of the 21st. Century P Czech Ostrava 3 Compulsory study plan
2019/2020 (B0541A170008) Computational and Applied Mathematics P Czech Ostrava 3 Optional study plan
2019/2020 (B0541A170008) Computational and Applied Mathematics K Czech Ostrava 3 Optional study plan
2019/2020 (B0714A060010) Telecommunication Technology K Czech Ostrava 3 Optional study plan
2019/2020 (B0613A140014) Computer Science P Czech Ostrava 3 Compulsory study plan
2019/2020 (B0613A140014) Computer Science K Czech Ostrava 3 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner