Computer Science

In the age of digital intelligence, how can we coexist and collaborate with machines? What are the boundaries and limits of human–machine communication? This course focuses on five core issues in the field of intelligent communication: human–machine interaction, human–machine trust, human–machine emotion, human–machine value alignment and digital intelligence for good, and cultural and generational differences in human–machine communication. By adopting an interdisciplinary perspective and drawing on cutting-edge research cases, the course aims to help students better understand the nature of human–machine communication and human–machine relationships. It also equips students with the abilities to calibrate trust in machines, critically reflect on human–machine relationships, and engage in human–machine communication in a responsible manner.

This course analyzes the changing roles and functions of museums in a digital era. Students examine virtual museums, mobile applications, e-learning, and digital strategies. We also explore trends and horizons of museum technology to shape a museum of the future. Students complete article reviews and a project for a better understanding of the museum of our age. 

Topics include What is a museum, Museums in the digital age, Museum informatics, Digital collections management, Digital preservation, 3D applications in museums, Interactive museums, Case studies, Trends, HCI in the museum context, Virtual museums. 

This course delves into the theoretical underpinnings and practical applications of deep neural networks. Deep learning has revolutionized industries ranging from healthcare to finance, driving advancements in natural language processing, computer vision, and autonomous systems. 

From understanding fundamental concepts to implementing advanced architectures like convolutional and recurrent networks and transformers as well, this course covers both theoretical knowledge and hands-on experience essential for navigating the complexities of deep learning. 

Topics include Deep learning basics, Neural networks, Training neural networks, Convolutional neural networks, Recurrent neural networks, Transformers, Applications: NLP, Applications: CV, Generative models. 

The course deals with time discrete signals and systems. Items such as the Fourier Transform, the Discrete Fourier Transform (DFT) and the z-transformed are treated in the course as well as some basic structures for implementation of digital filters. Also, system function and frequency functions are introduced as well as digital filters. Digital processing of analogue signals using A/D and D/A conversion is studied. In the laboratory work, practical applications of digital signal processing such as speech signals processing and biomedical signals processing are treated. Also, the course includes basic filter design using Matlab and digital signal processors (DSP).

This course introduces and practices user experience design through methods teaching, case studies, project practice, and industry internship. The course covers the following topics: the role of design in the new era; user experience-oriented innovation; the importance of user experience; internet thinking; how to elicit the user needs; interview and concept generation; storyboards and information architecture; interface representation; operational flow; .interaction details and prototypes, and user experience research and testing. 

This course is an advanced-level program, and it is recommended for students with basic concepts of user experience design. If you have no prior experience, more time may be necessary for learning. 

This course covers basic data representations, algorithms, and applications for interactive visualization. The class mainly focuses on computer graphics and spatial data visualization. 

Topics include Graphics systems, OpenGL basics, Transformations, Data acquisition, Data representation, Viewing, Lighting and shading, Shaders, Color models, Textures, Volume rendering, Surface visualization, Geometric processing, Image visualization, Advanced topics in visualization. 

This course covers concurrent activities, busy-wait and polling, synchronization and communication, atomic operations such as test-and-set, and mutual exclusion. Central aspects of the Java concurrent package, such as locks, semaphores, thread pools, tasks, and blocking queues are also reviewed. The course concludes with an overview of multicore hardware, real-time operating systems, and scheduling.  Entry requirements include Programming and a second course in Java.

This course covers the fundamental concepts of databases—an essential component in implementing e-business information systems—including the entity-relationship model, relational databases, and the use of structured query language (SQL). Through individual projects, students also explore how to integrate databases with business information systems. Topics include Introduction to Database Industrial Information Management, Introduction to Structured Query Language (SQL), Relational model and normalization, Database design using normalization, Data modelling with the entity-relationship model, Transforming data models into a database design, SQL for database construction and application processing, Database redesign, Managing multi-user databases, Web Server Environment, and Data warehouses, business intelligent systems, and big data. 

This course introduces computer vision with a focus on modern deep learning. We start with the foundational concepts and history of the field. We then dive into the key architectures that have shaped modern computer vision. We study convolutional neural networks (CNNs) and Vision Transformers (ViT), learning how they work and how they are used for fundamental tasks like image classification, object detection, and semantic segmentation. Then, we cover 3D computer vision, including problems like 3D reconstruction. Finally, students focus on deep generative models for vision, exploring how they are used to create realistic images and videos.  

Prior to taking this course, it is recommended that students take courses in linear algebra and probability and statistics.  

Topics include Introduction to Computer Vision; Basics of Digital Images and Processing; Machine learning and neural networks; Convolutional neural networks (CNNs); Computer vision problems; Vision transformers (ViTs) for computer vision; 3D Computer Vision; Generative Models: VAEs, GANs; and Generative Models: Diffusion Models, Multimodal models. 

This course examines how the Internet works and how everyday online activities generate data that are collected, analyzed, and monetized by digital platforms. It explores key issues related to data privacy, security, ownership, and control, addressing questions about how personal information is used and how individuals can protect themselves online. The course provides practical knowledge and tools for understanding Internet infrastructure, data tracking practices, and strategies for managing one’s digital presence with greater confidence and awareness.