Computer Science

This course introduces students to the foundations of game creation and provides an overview of different aspects of game development. Students learn C# Programming (industry standard), starting with console application, then GUI games on various platform with graphics, dialog boxes, and user control. The course includes an overview of topics including game architecture, interface design, graphics for games, audio for games, prototyping and play testing. Students implement their creative gaming ideas by using the latest gaming tools. The course requires students to take prerequisites.

This is an intermediate-level course on data science with a focus on machine learning methods and algorithms using Python. First, the course introduces the big picture of machine learning using various examples, while teaching techniques of how to do data visualization for various types of data, a very important subfield of machine learning. The course also addresses decision tree learning; learning linear separators; logistic regression; generalization and overfitting; model selection and regularization; linear regression; ensemble learning; unsupervised learning; neural network models, and principal component analysis. 

The course also covers prediction and classification tasks using artificial neural networks and deep neural network models, and how to inrperet the results of accurate but black-box machine learning algorithms. A thorough treatment of deep learning is covered through an advanced course, Advanced Data Science. 

This introductory course on intelligent robots and systems is at the intersection of machine learning, artificial intelligence, computer vision and control theory. Students learn the fundamentals of developing systems which can sense, plan and act in the world based on various topics from the domains. Emphasis is on algorithm design, probabilistic reasoning, decision making under uncertainty and learning to improve behaviors using data. The course requires students to take prerequisites. 

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 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.