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

• Class - 30 hours
• Lecture - 30 hours

(in Polish) Będziemy omawiać różne zagadnienia kombinatoryczne.

W szczególności, omówimy podstawowe wyniki teorii uczenia maszynowego oraz teorii Vapnika-Chervonenkisa, a także wyniki dotyczące rodzin zbiorów (set systems). Wyniki te są fundamentalne zarówno w teorii uczenia maszynowego, jak i w kombinatoryce geometrycznej i geometrii obliczeniowej, a także mają związek z innymi dziedzinami matematyki i informatyki, m.in. logiką i teorią grafów. Będziemy również omawiać inne ważne wyniki w kombinatoryce (w szczególności, lemat Szemerediego o regularności) i twierdzenie Erdosa-Hajnala.

• Lab - 30 hours
• Lecture - 30 hours

The lecture aims to present central issues in modern functional programming in languages such as Haskell, Coq, Idris, especially focusing on usung types for program specification and verification.

• Lab - 30 hours
• Lecture - 30 hours

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 (mixed with classes, browsing of source code, reading scientific papers) 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.

• Class - 30 hours
• Lecture - 30 hours

Game theory was initiated by von Neumann and Morgenstern as a mathematical theory of rational behaviour. A game comprises description of possible moves and payoffs for each of the players. Typically, each player searches for a strategy maximizing her payoff. The rational behaviour of players is well described by the concept of Nash equilibrium.

• Lab - 30 hours
• Lecture - 30 hours

The main aim of this lecture is to introduce students with selected models, algorithms and tools used in comparative genomics. In particular we will be focused on algorithms and tools related to trees (in many contexts). Classes will be partially converted to labs.

• Class - 30 hours
• Lecture - 30 hours

Auctions are a widely used mechanisms for resource exchange and allocation, that finds application in real world (e.g. internet auctions) as well as computational applications (e.g. resource allocation in multi-agent systems and eCommerce). Which auctions are best for the seller and which for the buyers? What is the impact of knowledge of others' valuations? How bad is collusion? Is it hard to determine the winner when bundles of several interdependent objects are being sold? Does it depend on the bidding language?

The aim of this course is to introduce and present the basic issues and problems of arising in auctions and to deepen their understanding via game theory and computational complexity theory.

• Class - 30 hours
• Lecture - 30 hours

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.

• Class - 30 hours
• Lecture - 30 hours

Universal algebra and category theory are by now two classical areas of mathematics that offer abstract concepts, methods and results which have been widely adopted in foundations of computer science and by now form the standard language to deal with, among others, modelling, design, and systematic construction of complex software systems.

The course recalls basic concepts of universal algebra and introduces the language of category theory, limited to the most elementary and important notions and related results. We hint at least at the possible appliocations of the categorical language in various areas of computer science, for instance in type theory and in foundations of algebraic specifications.

• Class - 30 hours
• Lecture - 30 hours

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).

• Class - 30 hours
• Lecture - 30 hours

Analysis of selected problems of computational geometry and methods which solve them (sweep line, divide and conquer, prune and search, duality etc.). Theory and applications.

• Lab - 30 hours
• Lecture - 30 hours

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 common approaches to solving convex problems in practice.

• Lab - 30 hours
• Lecture - 30 hours

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.

• Lab - 30 hours
• Lecture - 30 hours

This course consists of two components: a lecture and a practical work. The lecture will cover the principles, advanced concepts, and technologies of distributed systems, including communication, replication, fault tolerance, and security. The practical part, in turn, will give students an opportunity to test the new knowledge in the real world. More specifically, individually or in pairs, the students will build a distributed system related to cloud computing.

The practical work is very demanding in terms of the dedicated time and required programming skills.

• Class - 45 hours
• Lecture - 15 hours

(in Polish) Celem przedmiotu jest zaprezentowanie słuchaczom najważniejszych informacji dotyczących przedsiębiorczości, w szczególności procesu poszukiwania i operacjonalizacji szans rynkowych, tworzenia koncepcji biznesu, zakładania nowych przedsiębiorstw i zarządzania nimi na pierwszych etapach rozwoju. Przedstawiane podczas wykładów zagadnienia zostaną przeanalizowane na przykładach, a następnie praktycznie wykorzystane na ćwiczeniach.

• Class - 30 hours
• Lecture - 30 hours

FINTECH (Financial Technology) is a new branch in the IT business that aims at applying newest technologies in the financial services sector. While conventional financial institutions have strived for years to introduce new solutions, FINTECH companies aim to revolutionize banking and other financial services.

FINTECH products are built upon such technologies as Big Data, social network analysis, machine learning algorithms, artificial intelligence. For instance, there exist in the US pay-day-loan companies that assign credit rating to clients based solely on their Facebook data. Such algorithms were also patented by Facebook itself in 2014.

During the lecture, we will discuss the key trends in FINTECH revolution and we will work through multiple examples of successful FINTECH stories. A particular attention will be paid to business and regulatory aspects of FINTECH solutions.