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Permanent elective courses for Computer Science (course group defined by Faculty of Mathematics, Informatics, and Mechanics)

Faculty: Faculty of Mathematics, Informatics, and Mechanics Courses displayed below are part of group defined by this faculty, but this faculty is not necessarily the one that organizes these courses. Read Help for more information on this subject.
Course group: Permanent elective courses for Computer Science
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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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2023Z 2023L 2024Z 2024L
1000-2N09ZBD n/a n/a
Classes
Winter semester 2023/24
  • Lab - 30 hours
  • Lecture - 30 hours
Summer semester 2024/25
  • Lab - 30 hours
  • Lecture - 30 hours
Groups

Brief description

The course will cover various issues which have not fit into the basic database course. Furthermore, the database research domain is so huge that it would not fit into any basic course. The subjects of lectures will be relational database tuning, object-relational mapping, columnar data store, NOSQL stores (key-value, wide-column, document, graph), advanced server programming and distributed databases.

Course page
1000-2N09ZSO n/a n/a
Classes
Summer semester 2023/24
  • Lab - 30 hours
  • Lecture - 30 hours
Summer semester 2024/25
  • Lab - 30 hours
  • Lecture - 30 hours
Groups

Brief description

The course it is highly recommended for students who plan to attend Master Seminar on Distributed Systems, which is partly devoted to operating systems and in particular distributed operating systems. The course will have a form of lectures and labs.

We plan to view in detail the structure of a specific operating system. The chosen case study is Linux which is modern operating system, popular in the Faculty of Mathematics, Computer Science and Mechanics, University of Warsaw, often used as server platform, but also on desktops, mobiles, as embedded system. Source code of Linux is freely available which gives a unique opportunity to analyze in detail used algorithms, data structures, and also to run experiments and do research in the area of operating systems.

Course page
1000-2N00ALG n/a n/a
Classes
Summer semester 2023/24
  • Classes - 30 hours
  • Lecture - 30 hours
Summer semester 2024/25
  • Classes - 30 hours
  • Lecture - 30 hours
Groups

Brief description

The course is a continuation of the course "Algorithms and data structures". The aim is to make students acquainted with the methods of constructions of efficient algorithms for various combinatorial problems.

Prerequisities: Algorithms and data structures

Course page
1000-2N00SID n/a n/a
Classes
Summer semester 2023/24
  • Classes - 30 hours
  • Lecture - 30 hours
Summer semester 2024/25
  • Classes - 30 hours
  • Lecture - 30 hours
Groups

Brief description

The course is focused on using intelligent methods for solving problems that are difficult or impractical to solve with other methods. Accordingly, we discuss, among the others, various approaches based on heuristics, approximations, randomized, as well as deductive and inductive schemes of reasoning, often designed by analogy to the human way of problem solving. The main topics include also intelligent search through large spaces of states and solutions, intelligent game strategies, reasoning in logic and logical foundations of planning, foundations of machine learning in relation to artificial intelligence, foundations of modeling of uncertainty, as well as various specialized applications.

Course page
1000-2N03TI n/a n/a
Classes
Winter semester 2023/24
  • Classes - 30 hours
  • Lecture - 30 hours
Winter semester 2024/25
  • Classes - 30 hours
  • Lecture - 30 hours
Groups

Brief description

Introduction into a theory which is useful in many application of informatics, like cryptography, modeling of natural language, and bio-informatics. The theory defines quantitative measures of information contained in a random variable or in a sequence of bits. It also provides criteria of optimal compressing (coding) of information, and of sending a message through an insecure channel.

Course page
1000-2N03BO n/a n/a
Classes
Summer semester 2023/24
  • Lab - 30 hours
  • Lecture - 30 hours
Summer semester 2024/25
  • Lab - 30 hours
  • Lecture - 30 hours
Groups

Brief description

The aim of this course is to introduce students with a computer science and mathematics background to data-driven problems from molecular biology (we will focus on analysis of protein and nucleic acid sequences). In this course we will present some of the mathematical models and computational methods used today in molecular sequence analysis.

Course page
1000-2N09KDW n/a n/a n/a
Classes
Winter semester 2023/24
  • Classes - 30 hours
  • Lecture - 30 hours
Groups

Brief description

Introduction to text and image compression. Universal compression methods.

Course page
1000-2N00PLO n/a n/a
Classes
Winter semester 2023/24
  • Lab - 30 hours
  • Lecture - 30 hours
Winter semester 2024/25
  • Lab - 30 hours
  • Lecture - 30 hours
Groups

Brief description

This is an introductory course on logic programming.

The main topics are: definite clause programs, SLD-resolution, declarative and operational semantics, negation in logic programming (SLDNF-resolution) and control in logic programming.

Laboratory classes focus on practical aspects of logic programming

(simple programming techniques in Prolog).

Course page
1000-2N09SUS n/a n/a
Classes
Summer semester 2023/24
  • Lab - 30 hours
  • Lecture - 30 hours
Summer semester 2024/25
  • Lab - 30 hours
  • Lecture - 30 hours
Groups

Brief description

Machine learning usually refers to a system that is capable to learn from experience, analytical observation and historical data. This capacity should result in a system that can continuously self-improve and thereby offer increased efficiency and effectiveness. This course will be an introduction to the basics of machine learning. We will study multiple machine learning models including decision trees, neural networks, Bayesian learning, instance-based learning, and genetic algorithms. In doing so, we will begin to understand some of the issues and challenges facing attempts at machine learning-generalization, bias, overfitting, model selection, feature selection and learnability-while being exposed to the pragmatics of implementing machine learning systems.

Course page
1000-2N09WSS n/a n/a
Classes
Summer semester 2023/24
  • Classes - 30 hours
  • Lecture - 30 hours
Summer semester 2024/25
  • Classes - 30 hours
  • Lecture - 30 hours
Groups

Brief description

The lecture covers modeling and reasoning methods applied in the design of intelligent systems. It covers such applications of logical formalisms as planning, databases, knowledge representation, Semantic Web, autonomous systems, etc. Along with knowledge of the key automated reasoning techniques applied in the addressed application areas, it develops the corresponding modeling skills. Selected rule engines used as reasoning tools are also discussed.

Course page
1000-2N09ALT n/a n/a
Classes
Winter semester 2023/24
  • Classes - 30 hours
  • Lecture - 30 hours
Winter semester 2024/25
  • Classes - 30 hours
  • Lecture - 30 hours
Groups

Brief description

The lecture covers basic algorithms on texts (strings), as well as related algorithmic techniques and data structures (suffix trees, subword graphs). The classical problems are string-matching, repetitions, symmetries and compression. Also some problems will be discussed related to computational biology and textual generation of two-dimensional fractals. The course covers also some algorithmics on compressed texts, the complexity status of basic problems can unexpectably

change to NP or PSPACE completeness in the compressed setting. A relatively short part of the course is devoted to combinatorics on words.

Course page
ul. Banacha 2
02-097 Warszawa
tel: +48 22 55 44 214 https://www.mimuw.edu.pl/
contact accessibility statement site map USOSweb 7.1.1.0-c345f6b74 (2024-12-18)