MSc seminars for Machine Learning (course group defined by Faculty of Mathematics, Informatics, and Mechanics)
Course group schedules
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2023 - Academic year 2023/24 2024 - Academic year 2024/25 (there could be semester, trimester or one-year classes) |
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2023 | 2024 | |||||||
1000-3D21RC |
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Academic year 2023/24
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Brief description
The seminar's topic is works in the field of the broad issue of robot learning, which includes: Computer vision, Reinforcement learning, Robot control, Human-robot interactions. The seminar is conducted in the form of presentations delivered by participants. The presentation topics are related to current research in the field of robot learning. |
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1000-5D17UM |
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Academic year 2023/24
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Brief description
(in Polish) Seminarium dotyczy szeroko rozumianej tematyki uczenia maszynowego, w tym: * wstępnego przetwarzania i eksploracji danych, * konstrukcji modeli statystycznych, * zagadnienia wyboru cech i wyboru modelu, * algorytmicznych aspektów dopasowania modelu, * oceny skuteczności modeli predykcyjnych oraz diagnostyka struktury modeli. |
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1000-2D20PRD |
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Academic year 2023/24
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Brief description
The topic of the seminar will be issues related to the creation of practical prediction systems. As part of the seminar, we will introduce all relevant aspects of this process such as data preparation, feature generation, model building and explainability. The seminar will be based on the knowledge acquired as part of the challenges faced by MIM Solutions spin-out. |
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1000-2D22SI |
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Academic year 2023/24
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Brief description
The seminar is devoted to the broadly understood subject of creating intelligent systems based on artificial intelligence methods and modern machine learning, in particular. Much of the discussion will focus on practical issues related to industrial and business applications of such systems, as well as the tools needed to keep these systems in constant operation. The description includes examples of such AI methods. During the seminar, students present and discuss concepts from their own master's theses or present interesting AI-related topics, provided they have been approved by the seminar coordinators. There is also a list of proposed topics. The seminar provides an opportunity to discuss any problems encountered during the writing of the MSc thesis, as well as potential directions for its future development. |
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1000-5D96MN |
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Academic year 2023/24
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Brief description
The research seminar is devoted to numerical methods and their using in computational mathematics. We are interested different problems which have numerical and approximation aspects. The problems can be pure numerical or/and appearing in different type of applied mathematics where numerical methods are important parts. Finally, the problems appearing in "pure mathematics" where numerical method are used (for example to construct counter examples). A separate subject on the seminar is computer graphics. We are also interested on works related to parallel algorithms, complexity of continuous problems and quantum computations. |
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1000-2D10IZI |
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Academic year 2023/24
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Brief description
The main topic of the seminar will be the applications of computer science in other fields. We invite students who have strong non-IT interests, especially those studying at second faculties or professionally dealing with such topics. The lectures at the seminar will cover a wide variety of fields of application and even more diverse specific topics, i.e. they will provide an opportunity to broaden your mental horizons. Master's theses usually contain a very significant amount of work related to obtaining requirements specification and are associated with numerous contacts with non-computer scientists in order to learn about a foreign and often fascinating field of knowledge. |
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1000-2D22GSW |
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Academic year 2023/24
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Brief description
The seminar concerns research at the interface of computer science, artificial intelligence and economics. The topics of interest include game theory (both cooperative and non-cooperative), social networks analysis, social choice, as well as other topics related topics such as mechanism design, market design, and information economics. |
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1000-5D17ED |
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Academic year 2023/24
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Brief description
The seminar concentrates on various mathematical techniques helpful in data processing. The main focus is on intelligent data analysis, knowledge discovery from data and data mining. Another important topic of the seminar includes mathematical foundations and models underlying reasoning schemes on the basis of collected data in presence of vagueness, imprecision, ambiguity, and incompleteness. The seminar encompasses both theoretical and applicational studies in the corresponding areas. |
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1000-2D23DSR |
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Academic year 2023/24
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Brief description
The seminar is devoted to the theory and practice of data management and knowledge representation. We are interested in all flavors of data: not only relational, but also semistructured (XML, JSON), graph (RDF, LPG), object, text, temporal, stream, GIS, and others. The seminar presentations are based on recent papers presented at leading international conferences devoted to data management and knowledge representation: VLDB, PODS, SIGMOD, KR. Web page: https://www.mimuw.edu.pl/seminaria/deser-dane-strumienie-rozpraszanie |
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1000-5D22ADP |
Classes
Academic year 2023/24
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Brief description
The subject of the seminar covers the basic branch of computational biology which is proteomics, i.e. the analysis of proteins in living organisms. We focus on algorithms and mathematical models that allow for the interpretation of data obtained by the spectrometric technology. |
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1000-5D22ADB |
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Academic year 2023/24
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Brief description
The seminar topics include computational biology and machine learning in application to biomedical data analysis. We are interested in the problems of human diseases such as cancer or infectious diseases. From the area of computational biology we focus on analysis of modern molecular profiling data, analysis of single cell sequencing data, medical imaging, or protein structure. From methods, we focus on probabilistic graph models, statistical data analysis, machine learning, including deep learning, and generative models. |
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1000-2D97AL |
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Academic year 2023/24
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Brief description
In this seminar we study algorithms for discrete problems and data structures. We discuss methods of designing algorithms for discrete problems and analyzing their time complexity. We are interested in classical sequential algorithms as well as parallel, distributed, randomized and approximation algorithms. Efficient data structures are also studied. |
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