* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code | 460-4139/01 | |||||
---|---|---|---|---|---|---|

Number of ECTS Credits Allocated | 4 ECTS credits | |||||

Type of Course Unit * | Compulsory | |||||

Level of Course Unit * | Second Cycle | |||||

Year of Study * | First Year | |||||

Semester when the Course Unit is delivered | Winter Semester | |||||

Mode of Delivery | Face-to-face | |||||

Language of Instruction | Czech | |||||

Prerequisites and Co-Requisites | There are no prerequisites or co-requisites for this course unit | |||||

Name of Lecturer(s) | Personal ID | Name | ||||

PLA06 | prof. Ing. Jan Platoš, Ph.D. | |||||

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. | ||||||

Learning Outcomes of the Course Unit | ||||||

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. | ||||||

Course Contents | ||||||

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. | ||||||

Recommended or Required Reading | ||||||

Required Reading: | ||||||

- 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. | ||||||

- Prezentace k přednáškám.
[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 Reading: | ||||||

[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. | ||||||

[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. | ||||||

Planned learning activities and teaching methods | ||||||

Lectures, Tutorials | ||||||

Assesment methods and criteria | ||||||

Task Title | Task Type | Maximum Number of Points (Act. for Subtasks) | Minimum Number of Points for Task Passing | |||

Graded credit | Graded credit | 100 (100) | 51 | |||

Realizace úloh na cvičení | Other task type | 20 | 10 | |||

Explorativní analýza dat | Project | 30 | 15 | |||

Shlukování dat | Project | 25 | 15 | |||

Klasifikace dat | Project | 25 | 15 |