460-4139/01 – Machine Learning (SU)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantorprof. Ing. Jan Platoš, Ph.D.Subject version guarantorprof. Ing. Jan Platoš, Ph.D.
Study levelundergraduate or graduateRequirementOptional
Year2Semesterwinter
Study languageCzech
Year of introduction2022/2023Year of cancellation
Intended for the facultiesFEI, FMTIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
PLA06 prof. Ing. Jan Platoš, Ph.D.
PRO0199 Ing. Petr Prokop
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 2+2
Part-time Graded credit 18+0

Subject aims expressed by acquired skills and competences

The course aims to provide students with a detailed overview of procedures and methods in machine learning, from exploratory data analysis, through the search for similarity, comparison of objects to the search for classification models. Students will have the chance to implement and test individual methods on artificial and real data and evaluate the results they will learn to present correctly.

Teaching methods

Lectures
Tutorials

Summary

In the course, students get acquainted with the properties of data, their storage, and processing. They will also get acquainted with data analysis methods, machine learning, artificial intelligence, interpretation of results, and visualization. Lectures will focus on basic methods of analysis and data and extraction of findings extracted from data. Students will decide for themselves when which method is suitable, its assumptions, what its principle is, and what outputs can be obtained with it. The exercise will then be used for practical experiments on suitable data sets, experimentation with tools for data analysis, and evaluation of results.

Compulsory literature:

- Slides from Lectures [1] AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1. [2] BRAMER, M. A. Principles of data mining. London: Springer, 2007. ISBN 1-84628-765-0.

Recommended literature:

[1] LESKOVEC, Jure, Anand RAJARAMAN a Jeffrey D. ULLMAN. Mining of massive datasets, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232. [2] WITTEN, Ian H., Eibe FRANK, Mark A. HALL a Christopher J. PAL. Data mining: Practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, [2017]. ISBN 978-0-12-804291-5. [3] ZAKI, Mohammed J. a Wagner MEIRA JR. Data Mining and Analysis: Fundamental Concepts and Algorithms. 2nd edition. Cambridge, GB: Cambridge University Press, 2020. ISBN 978-0521766333.

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

Other requirements

Additional requirements are not placed on the student.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures (topics): 1. Frequent patterns in data. 2. Exploratory data analysis. 3. Representative clustering, Hierarchical clustering. 4. Clustering based on data density, cluster validation. 5. Special clustering methods, detection of outliers. 6. Linear classifiers (Linear discriminant analysis, Naive Bayes, Logistic regression). 7. Decision trees, rule classification. 8. Support Vector Machine, Kernel methods. 9. Neural networks. 10. Regression methods and Advanced methods in data classification. 11. Validation of classification algorithms. 12. Time series analysis. Exercises in the computer room (topics): 1. Implementation of the APRIORI method for searching for rules in data. 2. Exploratory analysis of data over a real dataset using appropriate tools. 3. Implementation of hierarchical clustering - Agglomerative clustering. 4. Implementation of DBSCAN algorithm. 5. Real example of clustering - independent work on exercises. 6. Dimension reduction. 7. Implementation of decision tree. 8. Testing the Support Vector Machine method over real data. 9. Neural networks. 10. Regression methods. 11. Ensemble methods and their use. 12. Classification - real example. 13. Time series analysis.

Conditions for subject completion

Part-time form (validity from: 2022/2023 Winter semester, validity until: 2022/2023 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Graded credit Graded credit 100 (100) 51 3
        Explorativní analýza dat Project 40  20
        Shlukování dat Project 30  15
        Klasifikace dat Project 30  15
Mandatory attendence participation: The student is obliged to complete the assignment at the seminars and submit the project assigned by the instructor.

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Conditions for subject completion and attendance at the exercises within ISP: Completion of all mandatory tasks within individually agreed deadlines.

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (N0612A140004) Information and Communication Security IKB P Czech Ostrava 1 Compulsory study plan
2024/2025 (N0613A140034) Computer Science DS P Czech Ostrava 1 Choice-compulsory type A study plan
2024/2025 (N0613A140034) Computer Science AZD P Czech Ostrava 1 Choice-compulsory type A study plan
2024/2025 (N0613A140034) Computer Science DS K Czech Ostrava 1 Choice-compulsory type A study plan
2024/2025 (N0613A140034) Computer Science AZD K Czech Ostrava 1 Choice-compulsory type A study plan
2024/2025 (N0688A140014) Industry 4.0 AZD P Czech Ostrava 1 Compulsory study plan
2024/2025 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics K Czech Ostrava 2 Optional study plan
2024/2025 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics P Czech Ostrava 2 Optional study plan
2024/2025 (N0541A170007) Computational and Applied Mathematics (S02) Computational Methods and HPC P Czech Ostrava 2 Optional study plan
2024/2025 (N0541A170007) Computational and Applied Mathematics (S02) Computational Methods and HPC K Czech Ostrava 2 Optional study plan
2024/2025 (N0613A140034) Computer Science SWI P Czech Ostrava 1 Choice-compulsory type B study plan
2024/2025 (N0613A140034) Computer Science SWI K Czech Ostrava 1 Choice-compulsory type B study plan
2023/2024 (N0612A140004) Information and Communication Security IKB P Czech Ostrava 1 Compulsory study plan
2023/2024 (N0688A140014) Industry 4.0 AZD P Czech Ostrava 1 Compulsory study plan
2023/2024 (N0613A140034) Computer Science DS K Czech Ostrava 1 Choice-compulsory type A study plan
2023/2024 (N0613A140034) Computer Science AZD K Czech Ostrava 1 Choice-compulsory type A study plan
2023/2024 (N0613A140034) Computer Science DS P Czech Ostrava 1 Choice-compulsory type A study plan
2023/2024 (N0613A140034) Computer Science AZD P Czech Ostrava 1 Choice-compulsory type A study plan
2023/2024 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics P Czech Ostrava 2 Optional study plan
2023/2024 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics K Czech Ostrava 2 Optional study plan
2023/2024 (N0541A170007) Computational and Applied Mathematics (S02) Computational Methods and HPC P Czech Ostrava 2 Optional study plan
2023/2024 (N0541A170007) Computational and Applied Mathematics (S02) Computational Methods and HPC K Czech Ostrava 2 Optional study plan
2023/2024 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2023/2024 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan
2022/2023 (N0613A140034) Computer Science DS K Czech Ostrava 1 Choice-compulsory type A study plan
2022/2023 (N0613A140034) Computer Science AZD K Czech Ostrava 1 Choice-compulsory type A study plan
2022/2023 (N0613A140034) Computer Science DS P Czech Ostrava 1 Choice-compulsory type A study plan
2022/2023 (N0613A140034) Computer Science AZD P Czech Ostrava 1 Choice-compulsory type A study plan
2022/2023 (N0688A140014) Industry 4.0 AZD P Czech Ostrava 1 Compulsory study plan
2022/2023 (N0612A140004) Information and Communication Security IKB P Czech Ostrava 1 Compulsory study plan
2022/2023 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics K Czech Ostrava 2 Optional study plan
2022/2023 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics P Czech Ostrava 2 Optional study plan
2022/2023 (N0541A170007) Computational and Applied Mathematics (S02) Computational Methods and HPC K Czech Ostrava 2 Optional study plan
2022/2023 (N0541A170007) Computational and Applied Mathematics (S02) Computational Methods and HPC P Czech Ostrava 2 Optional study plan
2022/2023 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2022/2023 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction



2023/2024 Winter
2022/2023 Winter