Computer Science

This course provides a technical overview of decision making and action control in robotics with an emphasis on the development of generalist robots. The course covers:  

(1) fundamental robot kinematics that discusses geometric relationships between the robot’s body and the end effector;  

(2) classical control and planning, that provides a theoretical basis, and  

(3) state-of-the-art robot learning that facilitates adaptive and continual learning of robot tasks.

Course Prerequisites: Machine learning; linear algebra; probability; computer vision; linux system; python programming, and pytorch programming.

This course provides a comprehensive introduction to both the practical and rigorous foundations of software engineering as well as both soft and hard skills. With them, students learn how to design, develop, test, verify, and maintain high-quality software systems.  

Topics include software development life cycles, design patterns, testing and coverage, code quality practices such as code reviews and coding style guides, and formal verification techniques.  

In this course, students engage in software engineering by applying coding, refactoring, and testing techniques to support continuous development and maintainability in real-world projects. Students also analyze given software designs or code using design patterns, testing strategies, and code quality standards. We also evaluate the strengths and limitations of various development methodologies and design alternatives to determine the most suitable approach for a given context. We design and implement tools or procedures that verify software correctness using logic-based reasoning and formal methods and build maintainable, high-quality software systems. 

Prerequisites: CAS1102 (Object-Oriented Programming), CAS2103 (Data Structure); students should be familiar with object-oriented languages including C++, Python, etc. Students should be familiar with implementing data structures and be able to analyze pros and cons of several data structures. Students should have a Github account.  

This course covers algorithm design techniques and algorithm analysis techniques. It deals with inductive and recursive thinking through which problems can be tackled and solved. 

In the class, students learn organized and effective thinking methods for problem solving. Topics include analysis tools (asymptotic complexity, recurrence), sorting and selection, retrieval and insertion of data (search tree, hash table), dealing with sets, dynamic programming, graph algorithms, text pattern matching, limit of computation (NP-Completeness), problem space, etc. 

Prerequisite: Data Structure. Students should also be familiar with the basics of discrete mathematics and probability. 

This course examines artificial intelligence and its real-world applications. It aims to give students a historical overview of the development of AI and its underlying concepts, to understand its current and potential impact on individuals, organizations, and society, and to analyze and discuss the future of AI and its potential applications. Additionally, the course will equip students with the knowledge to use AI for productivity and creativity and to engage with AI responsibly, considering ethical considerations and responsibilities.

This course covers the essential programming structures for managing data and controlling computation, as well as abstractions that facilitate decomposing large systems into modules. The course also covers pragmatics of programming languages, including abstract syntax, interpretation and domain-specific language implementation. Students do not learn how to use any one language, but instead learn the basic elements needed to understand the next 700 programming languages, or even design their own.

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.