460-4139/02 – 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 graduateRequirementCompulsory
Year1Semesterwinter
Study languageEnglish
Year of introduction2022/2023Year of cancellation
Intended for the facultiesFEIIntended 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
Part-time Graded credit 14+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

Full-time form (validity from: 2022/2023 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: The student is obliged to complete the assignment at the seminars and submit the project assigned by the instructor.

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Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2022/2023 (N0613A140035) Computer Science AZD P English Ostrava 1 Choice-compulsory type A study plan
2022/2023 (N0613A140035) Computer Science DS P English Ostrava 1 Choice-compulsory type A study plan
2022/2023 (N0688A140015) Industry 4.0 AZD P English Ostrava 1 Compulsory study plan
2022/2023 (N0612A140005) Information and Communication Security IKB P English Ostrava 1 Compulsory study plan
2022/2023 (N0541A170008) Computational and Applied Mathematics (S01) Applied Mathematics P English Ostrava 2 Optional study plan
2022/2023 (N0541A170008) Computational and Applied Mathematics (S02) Computational Methods and HPC P English Ostrava 2 Optional study plan
2022/2023 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan

Occurrence in special blocks

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