460-4162/01 – Machine Learning (ML)

The course aims to provide students with a comprehensive overview of machine learning methods and procedures, guiding them towards their practical application. Students will learn to perform exploratory data analysis, search for similarities and compare objects, and create and evaluate classification models. They will master classical machine learning methods, including linear and logistic regression, decision trees, and clustering methods. They will also gain knowledge of the principles of neural networks, including convolutional and recurrent architectures, and learn the basics of autoencoders. Skills: - independently implement and apply selected machine learning methods, - prepare and process data sets for analytical tasks, - select an appropriate model based on the nature of the problem and data, - evaluate accuracy and interpret results, - present analytical procedures and outputs in a professional environment. Competencies: - ability to solve complex tasks in the field of machine learning using both classical and modern methods, - orientation in the possibilities and limitations of individual approaches, - readiness to collaborate in a team on the design and implementation of data-oriented solutions, - ability to critically assess the quality of a model and its practical benefits.

The course introduces students to the characteristics of data, its storage, and processing options. Emphasis is placed on data analysis methods, classical machine learning techniques, and modern neural networks, including convolutional and recurrent architectures and autoencoders. Students will learn to interpret and visualize the results obtained and understand when it is appropriate to use individual methods, as well as their principles, assumptions, and expected outputs. Lectures will focus on methodology and principles, while exercises will provide space for practical experiments with real data sets, working with analysis tools, and critical evaluation of the results obtained.

[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.

Students' knowledge is assessed through graded assignments in seminars, data analysis, and the implementation of methods covered in independent work. The final assessment is then determined by a written exam supplemented by an oral part.