Elective courses for Machine Learning (course group defined by Faculty of Mathematics, Informatics, and Mechanics)
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2023Z - Winter semester 2023/24 2023L - Summer semester 2023/24 2024Z - Winter semester 2024/25 2024L - Summer semester 2024/25 (there could be semester, trimester or one-year classes) |
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| 1000-2M23DE |
 
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Classes
Winter semester 2023/24
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 2023/24
Groups
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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| 1000-2M19TCH | n/a |
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Classes
Summer semester 2023/24
Groups
Brief description
(in Polish) Program obejmuje dwa obszary zastosowań informatyki będące obecnie w kręgu głównych zainteresowań biznesu z uwagi na oferowane lub spodziewane źródła przewagi konkurencyjnej: chmurę obliczeniową i uczenie maszynowe. Ujęcie zagadnień z zachowaniem podejścia, w którym środowiska chmurowe (głównie typu IaaS i PaaS) są przede wszystkim kontekstem wykonawczym pozwoli skupić się słuchaczom na rozwiązywaniu konkretnych problemów manifestując tym samym podejście pragmatyczne. Całości towarzyszy wspólna praca wraz z partnerem biznesowym nad projektem integrującym tematy z zakresu programu przedmiotu, którego wykonanie jest wymaganym elementem uzyskania oceny. Wybór konkretnych zagadnień wykładu zależy od scenariuszy przedstawionych przez partnera biznesowego, lecz będzie obejmować co najmniej modelowanie matematyczne, szeregi czasowe i techniki przetwarzania języka naturalnego (NLP). |
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| 1000-2M22OW | n/a |
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Classes
Summer semester 2023/24
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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 2023/24
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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 2023/24
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 2023/24
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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 | n/a |
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Classes
Summer semester 2023/24
Groups
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 2023/24
Groups
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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| 1000-2M21PRS | n/a |
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Classes
Summer semester 2023/24
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Brief description
This course describes problems and issues related to the implementation of large-scale distributed systems and is based on the experiences coming from the actual implementations of such systems. We will discuss the practical aspects of building high-throughput systems which process petabytes of data daily in geographically dispersed data centers. We will discuss common problems and consider decisions related to the maintenance and development of such systems. We will look at techniques for effective data exchange between system components, issues related to the storage and processing of large amounts of data. We will also deal with the practical aspects of organisation of infrastructure supporting machine learning in the realities of large-scale systems. |
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