Computer Science

Serious games—also known as applied games—harness the mechanics of gaming for meaningful purposes across diverse fields, including medicine, scientific research, industry, the military, and education/training. This course introduces and analyzes exemplary cases and innovative applications of serious games, exploring their creative design philosophies. It also covers design thinking methodologies and tools, guiding students in using AI technologies to conceptualize and design games that address real-world challenges. The course further examines emerging technologies related to games, such as artificial intelligence, blockchain, artificial life, human computation, cloud computing, symbiotic computing, augmented/mixed reality (AR/MR), advergaming, collective intelligence, and game theory.

This course provides a comprehensive foundation in Human-Computer Interaction (HCI), focusing on designing user-friendly, efficient interactive systems. It covers human ability analysis, natural interaction techniques, and human-AI interaction, integrating interdisciplinary knowledge to solve real-world problems. The key goals of HCI-interaction efficiency and interface naturalness are explored to enhance user experience. 

By the end of the course, students develop advanced expertise in formulating and implementing statistical approaches to practical problems in a wide variety of subject areas. Students learn to integrate material covered in various lecture courses with skills developed through practical work in order to solve real-world problems. Students learn to: formulate questions of interest and identify relevant informal and formal statistical methodology for a wide variety of practical contexts; implement the various stages of advanced statistical analysis appropriately in R; interpret the output of R procedures; critically collate results and conclusions; present the main results and conclusions in the form of concise summaries; present results of analyses in the form of written reports; critically assess published applications of statistical analysis; and work independently on practical data analysis problems. The course uses a number of case studies covering different areas such as missing values, modelling, regression and related methods, data visualization, and analysis of ordinal and categorical data.

The course covers a selection from the following topics: homotopy sets, homotopy groups and the Hurewicz theorem, fibrations and cofibrations, generalized homology theories and spectra, bundles and classifying spaces, characteristic classes, low-dimensional topology, geometric group theory, spectral sequences and cohomology operations. The methods presented are illustrated by applications to various classical problems in algebraic topology.

This course examines for embedded system design using the SOPC (system on a programmable chip) approach. It covers embedded applications, microprocessors, microcontrollers, architecture, organization, programming memories, I/O interfacing, sensors, actuators, analog interfaces, hardware/software partitioning and interfacing, concurrency, and implementing data transformations and reactivity. 

This course focuses on the importance of the accessibility of digital information systems and the need to incorporate usability and accessibility principles at all stages of the design and development of these systems and the digital documents they contain. The course is divided into three parts: digital content architecture; digital content creation; legal and functional accessibility.

This course provides participants with a toolbox of skills and methods that cover the entire data lifecycle. This includes approaches ranging from data collection (e.g., web crawling, experimental design), to analysis (data handling, statistical tests, regression models), and finally to visualization. Fundamental and applied questions of statistics are addressed, along with practical skills in programming languages, such as Java and R. The course content is supplemented and practiced through case studies and real-world data examples. In addition, the lectures are enriched by guest contributions from industry partners. By the end of the course, participants are able to independently carry out data-driven projects-from conception to the interpretation and presentation of insights. The aim is to provide a fundamental understanding of data-driven projects and questions, as well as practical skills and methods. Among other things, the following aspects are covered: surveys and experiments: design, implementation, evaluation; web Crawling 101: making online data usable; data visualization, filtering and clustering data, data cleaning and preprocessing, Machine Learning 101, statistical tests and linear regression models.

This course emphasizes the integration of theory and practice, guiding students to learn and apply programming methods to solve common application problems with the assistance of artificial intelligence tools. Starting from the basic principles of computers and artificial intelligence, the course covers the basic syntax of the Python language and preliminary programming methods. It introduces the basic operation methods of large-scale language models in artificial intelligence through several applications such as text processing, data visualization, and internet data acquisition. This allows students to initially grasp the method of problem-solving through programming design, encourages them to consciously use large-scale language models in artificial intelligence to improve learning efficiency and programming skills, and enables them to solve more complex common application problems. The content of the course includes the principles of computers, basic concepts and technical principles of artificial intelligence, generative artificial intelligence and large language models, data types in the Python language, control structures, functions and modules, text processing and numerical computation, data processing and chart analysis, internet technology, and common algorithms.

This course examines modern processor architectures; principles of modern processor design; pipelining; memory hierarchies; I/O and network interfacing; compiler and OS support; embedded processors; performance; multiprocessing.