Computer Science

This course examines to the computational modelling of natural language, including algorithms, formalisms, and applications. It covers computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, discourse analysis, automatic summarization, machine translation, speech processing, and machine learning. 

An overview is given of a general communication link consisting of the three parts: transmitter, communication channel, and receiver. Examples of digital communication methods are introduced for realistic bit rates and noise levels. Some of the following applications are considered in the course: Mobile digital telephony (3G, EDGE, GSM), WLAN, modem, ADSL, digital TV, Bluetooth, navigation (GPS), surveillance systems.

This course provides a basic and broad introduction to the representation, analysis, and processing of sampled data. The course introduces statistical analysis, mathematical modeling, machine learning, and visualization for experimental data. Examples are taken from real-world problems, such as analysis of internet traffic, language technology, digital sound, and image processing.

This course introduces mathematical and programming skills that are employed by researchers in the Molecular and Biophysical Life Sciences to analyze and integrate data and to understand the physics of living systems. The course is divided into two parts that run in parallel. The mathematics part of the course consists of nine lectures that cover: basic algebra, goniometry, differentiation and integration (including functions of multiple variables), limits, (partial) differential equations (first order and second order), Taylor expansion, basic probability theory and statistics and vectors (including dot product and cross product). Each lecture is followed by a supervised practical session. The programming part consists of six lectures that introduce the basics of programming by discussing the modulare structure of programs (modules, functions, loops), different data types and variables, as well as good practices. For some calculations of the mathematics part of the course it is explained how to perform those calculations using Python. After each lecture, students work individually on a series of practical coding assignments that familiarize them with the basics of programming in Python during supervised tutorials, where regular instruction and feedback is provided.

The cooperation of people from different disciplinary backgrounds is becoming increasingly important in developing knowledge and solutions in a complex world. This course explores how knowledge is created in and across disciplines, specifically by examining intersection between Computer Science and the disciplines of the Humanities commonly known as the Digital Humanities. It illustrates the potential benefits of and challenges to these interactions, by examining both emerging digital technologies and the traditional roots of cultural production such as language, historical records and institutions, and the arts. 

This course covers algorithm expression methods, functions and processing processes, analysis of difficulty, techniques for designing efficient algorithms, applications, and categorizes and utilizes previously developed algorithms by topic. 

This course examines the C programming language. It covers basic programming topics, such as variables, control, loops, and functions, to more advanced topics.

This course examines the genre and history of games. It covers basic game design, storytelling and narrative analysis, game engines, design of virtual worlds, real-time 2D graphics, game physics and physical simulation, pathfinding and game AI, content generation, 3D game concerns, multiplayer and distributed games, and social issues.

This course introduces both computer game design and development. It includes important computer design concepts and fundamentals to create electronic games using C# and Unity. Students manage paper and digital prototyping, design iteration, and user testing. They also use game scripting and programming, including computer graphics and animation. The audience for this course includes current and aspiring game designers and those interested in all principles of the game creation process. Students from different locations share observations for a multinational/multicultural perspective.

This course covers basic modeling such as statistical processes, distribution theory, probability theory, and application methods. Topics include basic stochastic modeling, the theory of queues and its application to real cases. Students will have hands-on experience with popular simulation tools and be trained with both analytic models and popular simulation tools (SMPL, OMNet++, NS3).