Computer Science

The first half of this course focuses on digital systematic knowledge organization systems including main systems such a Universal Decimal Classification (UDC), digital structures, and their characteristics. The second half of the course examines representations of knowledge. Topics include: folksonomies-- digital collaborative classification and tagging; thesauri; formal structures for knowledge representation-- graphs; automatic classification.

This course covers the fundamental principles of computation, including formal languages, abstract machines (automata), and computability theory

This course develops the understanding of Computer Networks and the Internet: Internet, network edge, network core, network performance metrics, protocol layers and service models, LAN topology, Physical media, OSI reference model and TCP/IP reference model, network standardization, computer network attacks and prevention, history of computer networking and the Internet. Application and Transport Layers: Principle of network applications, socket programming, transport layer services, multiplexing/demultiplexing, connectionless transport, connection-oriented transport (TCP), TCP congestion control and performance issues. Network Layer: Network layer design issues, forwarding and routing, virtual circuit and datagram networks, router architecture, Internet protocol, routing algorithms, routing the Internet, integrated and differentiated services. Data Link Layer: Data link design issues, error detection and correction, multiple access links and protocols, switched local area networks, IEEE 802 family, link virtualization, MPLS, data center networking. Physical Layer: Baseband systems, formatting textual data, formatting analogue information, sources of corruption, pulse code modulation, quantization, baseband modulation and demodulation/detection, inter-symbol interference, equalization, bandpass modulation and demodulation/detection amplitude. Emerging Communication Networks: Fundamentals of mobile networks, fundamentals of smart grid communication networks.

This course teaches the basics of programming as part of the field of informatics. Programming is used in many areas today, such as software development, automation, and data analysis, so understanding its fundamentals is very important. This course discusses algorithms, data structures, and control flow, and provides opportunities to practice coding, debugging, and basic software design. The course aims to build a solid foundation in programming that supports future learning and career growth.

Students complete an internship with a local organization or company. Each placement includes oversight and regular check-ins with an internship supervisor from the company or organization. The Internship Methodology Seminar accompanies the internship placement and offers a platform for reflection, enhancement of skills, and development of cultural competence. It focuses on practical skill application, cultural understanding, and adaptability within professional environments to provide a bridge between academic learning and real-world experience.

This course focuses on advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep learning techniques can be used to design important parts of advanced autonomous systems that exist in physical and cyber environments.

In this course, students study time and space complexity classes; identify the complexity classes associated with computational problems; prove that problems are complete for particular complexity classes; develop the ability to fit a particular problem into a class of related problems, and so to appreciate the efficiency attainable by algorithms to solve the particular problem; study circuit complexity and the class NC of parallelizable problems; study randomized computation and the associated complexity classes; and explore how the P=NP problem is related to cryptography.  

This course aims to develop flexible and logical problem-solving skills, understanding of main bioinformatics problems, and appreciation of main techniques and approaches to bioinformatics. Through case studies and hands-on exercises, students (i) master the basic tools and approaches for analysis of DNA sequences, protein sequences, gene expression profiles, etc. (ii) understand important problems and applications of computational biology, including identifying functional features in DNA and protein sequences, predicting protein function, and deriving diagnostic models from gene expression profiles, (iii) be confident to propose new solutions to both existing and emerging problems in computational biology. This course requires students to take prerequisites.

This course provides an introduction to systems with multiple agents/units/robots that mutually depend on each other’s behaviors in order to evaluate their own or collective system performance. The course covers theory for strategic interaction between self-interested agents as well as more altruistic agents working explicitly together in complex distributed environments. Game theory and swarm intelligence are central parts of the course curriculum.

This course provides research training for exchange students. Students work on a research project under the guidance of assigned faculty members. Through a full-time commitment, students improve their research skills by participating in the different phases of research, including development of research plans, proposals, data analysis, and presentation of research results. A pass/no pass grade is assigned based a progress report, self-evaluation, midterm report, presentation, and final report.