Computer Science

This course introduces the fundamental theory and concepts of computational intelligence methods, in particular neural networks, fuzzy systems, genetic algorithms and their applications in the area of machine intelligence. Topics include: (1) Understand the concepts of fuzzy sets, knowledge representation using fuzzy rules, approximate reasoning, fuzzy inference systems, and fuzzy logic control and other machine intelligence applications of fuzzy logic. (2) Understand the basics of an evolutionary computing paradigm known as genetic algorithms and its application to engineering optimization problems. (3) Understand the fundamental theory and concepts of neural networks, neuro-modeling, several neural network paradigms and its applications. (4) Contents: Introduction to Fuzzy Logic. Introduction to Fuzzy Sets. Introduction to Fuzzy Inference Systems. Fuzzy Logic Applications. Introduction to Genetic Algorithm. Fundamental Concepts of Artificial Neural Networks and Neural Network Architectures.

This course introduces algorithms and algorithmic thinking. Students examine common algorithms, algorithmic paradigms, and data structures that can be used to solve computational problems. Emphasis is placed on understanding why algorithms work, and how to analyze the complexity of algorithms. Students learn the underlying thought process on how to design their own algorithms, including how to use suitable data structures and techniques such as dynamic programming to design algorithms that are efficient. The course includes a prerequisite.

This course introduces the essential software engineering body of knowledge, including software project management, software requirements and specifications, software design, and software testing and maintenance.

This course introduces the fundamental principles of microcontroller systems and their peripherals. It combines theoretical foundations with practical training in the design and implementation of application software using the C programming language. Emphasis is placed on developing control-oriented applications and understanding the interaction between microcontrollers and their peripheral modules. The course builds skills to design, program, and manage microcontroller-based applications, and apply knowledge across a range of typical use cases. The course concludes with two integrative mini-projects that serve as capstone exercises, synthesizing the concepts acquired and demonstrating abilities to implement effective microcontroller solutions.

Data analysis and information visualization have become essential skills for news communication practitioners in the new media era.This course will introduce the basic knowledge of research design and data analysis, as well as the specific process of data collection, collation, analysis and visual presentation, and cultivate students' basic ability of data analysis and visual presentation by combining the changes of media forms and the characteristics of the era of big data. This course will provide a foundation for their practical work and further academic research, such as data news, public opinion analysis and market analysis.

The course is divided into four main sections: Fundamentals of research design and data analysis, data acquisition and cleaning, data statistical analysis, and information visualization.

This course provides an overview of graphics hardware, basic drawing algorithms, 2-D transformations, windowing and clipping, interactive input devices, curves and surfaces, 3-D transformations and viewing, hidden-surface and hidden-line removal, shading and color models, modelling, illumination models, image synthesis, computer animation. 

This course introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. Topics include Multilevel implementation strategies; definition of new primitives (e.g., gates, instructions, procedures, and processes) and their mechanization using lower-level elements. The course includes analysis of potential concurrency; precedence constraints and performance measures; pipelined and multidimensional systems; instruction set design issues; architectural support for contemporary software structures.

This course cover the analytical and programming skills required for applying standard optimization algorithms to engineering and AI problems. It covers basic topics on optimization including the theory of unconstrained and constrained optimization, dual optimization tasks, linear programming, convex optimization, line search methods, trust-region methods, gradient descent, and Newton's method. Pre-requisite: ENGG1120 or ENGG1130 or ESTR1005 or ESTR1006 or MATH1510. Not for students who have taken AIST3010 or ESTR3112 or ESTR3114.

This course is an introduction to the interdisciplinary field of quantum information and computation. It covers the basic rules of quantum theory and the counterintuitive notions of quantum superposition and entanglement. In particular, it shows how quantum systems could be used to detect an object without directly interacting with it (Elitzur-Vaidman bomb tester), to increase the amount of bits that can be sent through a transmission line (dense coding), and to increase the chance to win certain games (CHSH game and GHZ game). It provides an overview of quantum computation and of major quantum algorithms such as Grover's search algorithm and Shor's factoring algorithm for prime factorization. Finally, the course introduces the upgraded framework of quantum theory, and uses it to explore applications to quantum error correction, quantum state discrimination, quantum cryptography, and quantum teleportation. Pre-requisite(s): MATH1013 or MATH1853 or MATH2014 or MATH2101.

This course provides the necessary background and experience in data science technology and concepts. Students gain experience tackling a complete data science project, from data gathering and pre-processing to data analysis through machine learning tools. Students apply fundamental concepts in machine learning to data storage and distributed processing as a foundation for their project.