Elective courses for Machine Learning (course group defined by Faculty of Mathematics, Informatics, and Mechanics)
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2024Z - Winter semester 2024/25 2024L - Summer semester 2024/25 2025Z - Winter semester 2025/26 2025L - Summer semester 2025/26 (there could be semester, trimester or one-year classes) |
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| 1000-2M23DE | n/a |
 
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Summer semester 2024/25
Groups
Brief description
Overview of the data processing pipeline; collection and storage of raw data; processing, cleaning, and storage of processed data; scaling tools for the data processing system. |
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| 1000-2M13DZD |
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Classes
Winter semester 2024/25
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Brief description
The subject consolidates both theoretical and practical knowledge about machine learning and data mining methods in applications related to large, heterogeneous, distributed and dynamically growing data. We discuss problems concerning reliability and quality of data in tasks of teaching effective models for classification, prediction and related applications as well as maintaining the effectiveness of such models applied as components of larger IT systems. We refer to a wide range of practical sources and shapes of data, in particular machine-generated data. We cover a wide range of practical tasks in machine learning and data analysis, e.g. anomaly detection or recognition of similarities. Based on practical examples, we discuss the full life cycle of data and information in processing and analysis systems, including properly integrated solutions based on machine learning and data analysis. |
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| 1000-2M12TGK | n/a | n/a | n/a |
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Classes
Summer semester 2025/26
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Brief description
Coalitional Game Theory is an actively expanding subfield of game theory. Coalitional games are used to model all these real-world situations where there exists potential synergy among participating agents. Applications of coalitional game theory span from economics and political sciences to medicine and computer science (electronic commerce, multi-agent systems). |
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| 1000-2M22OW | n/a | n/a | n/a |
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Classes
Summer semester 2025/26
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- (from 2025-10-01) Courses for PhD students in Computer Science
Brief description
This is an introduction to convex optimization, giving an overview of the landscape of convex optimization problems, and covering the most important convex optimization algorithms and lower bounds, as well as convex modelling techniques. The lab sessions cover convex modelling using modern software and implementation of selected convex optimization algorithms. |
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| 1000-2M03DM | n/a |
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Classes
Summer semester 2024/25
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Brief description
Presentation of the main issues in the field of data mining and the methods to resolve them. Discussion about the efficient implementation on large collections of data for basic problems, such as associative rules, data preparation, discretization of real value attributes, decision tree. Presentation of modern computation techniques such as parallel processing, evolutionary computation, using standard heuristic databases or specially constructed data structures. |
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| 1000-2M23DLS | n/a |
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Classes
Summer semester 2024/25
Groups
Brief description
No brief description found, go to course home page to get more information.
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| 1000-2M20IRIO |
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Classes
Winter semester 2024/25
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Brief description
The course takes an engineer’s perspective on building the complex set of systems and services that together provide the public cloud. The cloud infrastructure is a complex distributed system with unique requirements: high availability, massive scale and having deep software stacks. We plan to show how these requirements influence the key design decisions (communication, scalability, resource management, data management) and reliability engineering (monitoring, testing). The course will be delivered by Google engineers whose day-to-day work involves designing, building and maintaining cloud infrastructure. In contrast to teaching specific cloud technologies, our goal is to rather show principles driving the design of large-scale distributed systems. We plan to illustrate these principles using specific systems developed by your lecturers. |
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| 1000-2M14TGS |
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Classes
Winter semester 2024/25
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Brief description
The course is for those interested in method of analysing social networks, including such services as Facebook or Twitter. Social network analysis (SNA) is the set of methods, tools and techniques to study groups (be them groups of local communities, customers, employees of a company, members of a tribe, animals in a herd, etc.) The key idea behind SNA is to reveal and study a complex structure of the group by considering bilateral relationships between its members. While the SNA lays at the interface of mathematics, sociology, anthropology, statistics, economics, etc.), many of its recent advancements are due to a widespread application of game theoretic models to the study of networks. |
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| 1000-2M21IUM | n/a |
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Classes
Summer semester 2024/25
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Brief description
During the course, techniques of interactive data mining and constructing machine learning models will be presented. In particular, active learning and visual data mining techniques will be discussed. |
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| 1000-2M24RTS | n/a |
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Classes
Summer semester 2024/25
Groups
Brief description
The increasing complexity of artificial intelligence models and systems is forming new interesting challenges regarding the analysis of the security, robustness and behaviour of these models. In this course, we will analyse and discuss current articles and research directions on trustworthy artificial intelligence. Due to the dynamic development of this topic, the subject does not have a fixed programme, but will prioritise research challenges current at the time of the course, in particular articles published at this year's conferences such as CVPR, NeurIPS, ICML, ECML. |
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