Computer Science

This course uses Python as the medium to enable students to master the general ideas and methods of solving problems with computers. Students can master IPO (Input-Processing-Output) program structure, master basic control flow syntax, and be able to select data structures and related,  apply algorithms to complete simple computing tasks and have a solid programming foundation. For complex computational tasks, students can use a top-down modular decomposition approach to transform them into simple problem calculations. Students can use Python third-party libraries for data analysis and processing and AI applications (machine learning, natural language processing, etc.) solution, computer vision, etc.), and can be connected with more advanced artificial intelligence courses.

The course is designed for senior and graduate students majoring in Computer Science to learn design philosophy, practice, and research challenges for software design for smart medical sensing systems.   

Smart sensing systems have the capability of processing the sensing data on the device and the capability of providing the detected events as the outputs. This type of sensing system is required to generate accurate sensing events in real time. The systems are also required to minimize their energy consumption in specific application scenarios.  With smart sensing systems, the faults can be contaminated, the system can be more robust and easier to develop. Finally, the systems can be certified for medical use.   

This course covers model smart sensing devices, realtime computation, Computing-In-Memory devices, and communications between computing devices.  

This course covers the principles and practice of modern computer communications through studying network abstractions, protocols, architectures, and technologies at all levels of the five-layer reference model.

This course offers an introduction to computers with topics including: representation of digital information; specification and implementation of combinational systems; basic combinational modules; specification and implementation of sequential systems; basic sequential modules; design practices of combinational and sequential circuits.

This course will comprehensively introduce the basic concepts, mainstream structures, learning paradigms and key applications of deep learning technology based on neural networks that have been developed in recent years.

This course is a study of current topics related to ethics and legislation within the field of computer science and technology. Topics include: privacy; digital rights and inequality; copyright; cyber crime, security, and control; professional ethical codes.

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.