Computer Science

This course introduces key concepts and applications for the design of intelligent computer systems, or Artificial Intelligence (AI). Topics covered include heuristic search, game playing, logic, machine learning, deep learning,
natural language processing, robotics and image processing. Through interactive lectures, discussions, and assignments, students apply basic AI concepts and principles to develop modeling and analytical skills for problem-solving. Students create working programs that solve problems, reason logically, and/or improve their own performance. The class covers the history, different careers, and social/cultural impacts of AI as well. Finally, it prepares students to further explore and apply AI in research and application domains.

This course is designed for students with no prior experience of thinking in a computational manner. Students examine computational thinking as a problem-solving process with the aid of a computer, i.e. formulating a problem and expressing its solution in such a way that a computer can effectively carry it out. Topics include basic program structure: control constructs and data types, CT concept, limit of computing, computing trends, social-ethical issues and ramifications of computing. By the end of the course, students will be able to derive simple algorithms and code the programs to solve some basic problems. This course is cross listed among several departments at NTU.

This course teaches basic knowledge of the structure and functions of computers. Starting with the history of computers, the course explains the mechanisms of how they work and their future.  The course covers the representation of numbers; Boolean algerbra; combinatorial circuits; sequential circuits; computer architecutre; arithmetic, control, and memory systems; high performance computing; compilers; I/O and operating systems, and computer networks. 

This course introduces fundamental programming concepts using the C programming language. Students learn basic programming principles, syntax, and essential techniques for writing structured and efficient code. Through hands-on exercises and problem-solving activities, students develop algorithmic thinking and practical programming skills.

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.