Computer Science

This course covers the basic principles of machine reasoning, exploring the foundations of the rapidly developing field of artificial intelligence, and outlining the mathematical techniques used in both knowledge representation and future artificial intelligence courses. Once equipped with the main technical and theoretical tools, students are presented with a selection of different applications of machine reasoning, e.g., natural language processing, machine vision, and robotics, to create a point of contact with real-world examples and future, more advanced AI courses.

The course enables students to become skilled in the use of techniques and tools for modelling, implementing, and evaluating interactive systems, and they learn how to apply the theories, techniques, and tools presented in the course via challenging exercises which combine design, implementation, and evaluation.

This course offers an introduction to AI including its history, types of AI, and applications and challenges of AI. Topics include: problem-solving with search; uninformed search; informed or heuristic search; optimization and local search; genetic algorithms; neural networks; reinforcement learning; basic approaches to planning and scheduling; advanced applications.

This course, as one of key cornerstones of computer programming, based on Python language, focuses on concepts, methodologies and thinking pattern of computer programming. The main objective of the course is to help students to master basic programming skills, and to promote their abilities of logical, systematical and abstract thinking. The course mainly involves the basic concepts of computer programming, the basis of Python language and Python program structures, mathematical and numerical calculations, string and file operations, lists and data manipulation, functions and recursive functions, branch structures and loop structures, programming methodologies, procedure-oriented programming and object-oriented programming, graphical user interface programming methods and algorithm design, etc.

This course focuses on data analysis and guides students to learn a series of data analysis methods. It covers the use of different data analysis tools for data analysis. It covers data acquisition, data cleaning, data selection, data manipulation, data calculation, data grouping and time series, etc. Based on the theoretical understanding, students will learn to use Excel and Python programming language to process data. 

This course focuses on data and programming structures for the development of macros and applications developed with Visual Basic for Applications (VBA) in the Microsoft Office suite. Other topics include: creating PivotTables for data analysis; creating functions, forms, and menus; an introduction to R language. Pre-requisites: Information Skills; IT Management.

The course is designed to equip students with experience, knowledge, and skills for succeeding in globally interdependent and culturally diverse workplaces. Throughout the course, students will be challenged to question, reflect upon, and respond thoughtfully to the issues they observe and encounter in the internship setting and local host environment. Students will have the opportunity to cultivate professional and personal development skills as defined by the National Association of Colleges and Employers (NACE). Assignments focus on building a portfolio that highlights those competencies and their application to workplace skills. Students complete 45 hours of in-person and asynchronous online learning activities and 225-300 hours at the internship placement. 

This course provides an introduction to operating systems, viewed from both the user and the programmer's point of view, and from the machine side. It also provides insight into how today's data communication is taking place and how to create programs that use such communication. The programming language C is used, and a basic introduction is given.

This course examines basic algorithms and models of artificial intelligence. Starting from the basic concepts of artificial intelligence, it systematically introduces various learning tasks to help students understand and master mainstream AI models and algorithm tools, gradually establish artificial intelligence thinking patterns, and improve their ability to analyze and solve practical problems.

This course covers the fundamental concepts of how computers perform at machine and assembly language level. It looks at the design of an instruction set architecture and figures out what makes a Reduced Instruction Set Computer (RISC) differ from a Complex Instruction Set Computer (CISC). The course discusses basic design principles by understanding the components in a computer and the performance of a computer system.  

This course provides the required background for students who are interested in designing computer systems, doing serious development of operating system kernels and device drivers, and making better applications of computer systems.   

Course prerequisites: A background in Digital Logic, C Language and Assembly Language is required.