Computer Science

This course addresses the design and performance tuning of database applications, focusing on relational database applications implemented with relational database management systems. Topics covered include normalization theory (functional, multi-valued and join dependency, normal forms, decomposition and synthesis methods), entity relationship approach and SQL tuning (performance evaluation, execution plan verification, indexing, de-normalization, code level and transactions tuning). Additional selected topics include the technologies, design and performance tuning of non-relational database applications (for instance, network and hierarchical models and nested relational model for an historical perspective, as well as XML and NoSQL systems for a modern perspective). The course requires students to take prerequisites.

Cryptology—the modern discipline that combines construction and evaluation of cryptographic mechanisms—is a highly interdisciplinary field, deeply rooted in mathematics, but with branches in electronic engineering, computer science, and software and systems engineering. The course introduces fundamental aspects of cryptology from a modern perspective, focusing on design and security aspects of cryptographic schemes used for secure two-party communication, and of their underlying primitives.

This course offers a broad, accessible introduction to generative AI and LLMs, with a focus on their transformative applications in language-related disciplines. Tailored for students from humanities backgrounds, it explores how LLMs can advance fields such as linguistics, translation, language learning, and academic writing. Combining foundational theory with hands-on practice, the course equips students to utilize LLMs for both research and practical tasks. Beginning with an overview of AI and LLMs, the course introduces basic Python programming in a beginner-friendly way. It then transitions to practical applications, including using LLMs for language research, teaching and learning, translation, and exploring aspects of human cognition. Topics such as multilingualism, feedback generation, error correction, and linguistic analysis are also covered.

This course is designed for advanced undergraduates majoring in mathematics, statistics, and computer science. It primarily covers modern topics in computational statistics with an introduction to the statistical programming language R.

Prerequisites: Introductory probability and statistics courses are assumed to be taken, such as DATA130005 and DATA130024 or equivalent ones. Some coding experience is recommended.

This course introduces the characteristics of different structures of the Operating Systems (OS), such as microkernel, layered, virtualization, etc., and identifies the core functions. Topics: principles behind the core functions and comparison of the algorithms on which the core functions of the OS are built; how OS manages processes/threads and the mechanisms and policies in efficiently sharing of CPU resources; principles and techniques used by OS in effectively virtualizing memory and resources; the underlying causes of concurrency and deadlock issues; principles and techniques used by OS to support concurrency and synchronization control as well as the principles and techniques used by OS to support persistent data storage. During this course, students demonstrate knowledge in applying system software and tools available in modern operating system (such as threads, system calls, semaphores, etc.) for software development. Prerequisites: COMP2113 or COMP2123 or ENGG1340; and COMP2120 or ELEC2441.

This course introduces the various internal components of an operating system, including process and thread management, memory management, file system, security, and synchronization. Prerequisite: ESTR2102 or CSCI2100 or 2520. Not for students who have taken ESTR3102.

This course covers mathematical concepts and algorithms that allow society to recover the 3D geometry of camera motions and the structures in its environment. Topics include projective geometry, camera model, one-/two-/three-/N-View reconstructions and stereo, generalized cameras and non- rigid structure-from-motion. The course requires students to take prerequisites.

The course introduces students to a collection of basic programming concepts and techniques, including designing, testing, debugging, and documenting programs.  The course introduces the programming language Java, and is for both absolute beginners and those with prior computing experience. Java is a language used for other components of undergraduate modules. 

The course is an introduction to image processing and computational vision: the theory, principles, techniques, algorithms, and applications. Image processing allows the analysis and enhancement of images/videos, while computer vision facilitates the understanding of the content of images/videos. Application areas are far-reaching and wide, from data compression to measuring the quality of performing actions by humans. The techniques in image processing and computer vision may be used in autonomous driving, medical imaging, CGI, remote sensing, pedestrian behavior analysis, facial recognition and regeneration, traffic analysis, biometrics, product quality assurance, and much more.

This course presents an advanced introduction to modern information networking technologies. It also covers introductory topics for networking research methodologies such as system performance analysis techniques and networking algorithms.