Hong Kong

This course is an introduction to basic concepts in corporate finance and their applications to: (1) valuation of assets and cashflow discounting; (2) evaluation of investment proposals; (3) valuation of risky assets including stocks and bonds; and (4) corporate finance policy decisions including dividend and capital structure policy. Students are advised to take ECON2011 and 2021 before taking this course.

This course examines the relationship between ideas and social changes in modern Europe. It also explores the impact of modern European thoughts on contemporary culture in a cross-cultural perspective.

This course introduces the history of Hong Kong by focusing on its “fields”. It examines how various field sites (such as temples, museums, historical trails, and renovated historical buildings) could be used to understand the history of different places, events, and people in Hong Kong from the ancient period to the present. After a brief introduction to the historical development of Hong Kong, the course discusses aspects of rural Hong Kong history before moving on to urban Hong Kong. The last section of the course explains the diversity of Hong Kong’s communities, cultures, and religions. The course organizes several day trips to Hong Kong Island, Kowloon Peninsula, and the New Territories.

This course cultivates the fundamental literacy skills required for commercial music composition. The topics covered include pop songwriting, beat making, film scoring, and jingle production. Students are introduced to contemporary practices in the music industry through music demos, instrumental tracking, music arrangement, and music production techniques.

The course examines analytical and decision making skills in approaching practical and important social and business issues. In the course, students derive solutions or conclusions that require critical thinking, creativity, quantitative analysis, and common sense. Other course topics include: decision traps, quantitative decision models, statistical reasoning, computer tools, data-analysis techniques, and, more importantly, how these decision analysis concepts and tools can be applied in a broad set of social and business problems.

This course introduces the mathematical, statistical, and computational challenges in natural language processing. It covers the main applications of NLP techniques and a range of models in structured prediction and deep learning. Students gain a thorough introduction to cutting-edge machine learning and deep learning techniques for NLP. This course covers a broad range of topics including text classification, sentiment analysis, neural network, word embedding, sequence models, language models, machine translation, topic detection, and ChatGPT. The underlying techniques from probability, statistics, machine learning, transformer and deep learning are also introduced. Prerequisites: Pass in STAT2602 and COMP2119 or same level. Proficiency in Python.

This course is unique as it is co-organized by three faculties: Engineering, Medicine, and Science. This interdisciplinary collaboration highlights the importance of “biomimicry” and nature-inspired technologies that go beyond traditional disciplinary boundaries. Students in this course benefit from a comprehensive and diverse range of knowledge, merging insights from engineering, medicine, and science. By exploring how nature inspires technological advancements, students gain interdisciplinary skills and a broader perspective. The course is structured around three themes: industrial technology, biomedical technology, and environmental technology. Throughout this course, students learn to develop innovative ideas rooted in biomimicry to address real-world problems. Working in cross-faculty groups, students collaborate to design and build solutions that leverage the principles of biomimicry. This course equips students with the tools to contribute to sustainable and innovative technologies, preparing them for the challenges of the modern world.

This course uses an application-oriented approach to introduce students to the core concepts of psychometrics, a rigorous, scientific discipline of psychological testing and measurement. Students are provided with hands-on experiences to apply statistical methods for constructing and developing psychological measurement scales empirically as well as introductory exposure to instruments used by psychologists to assess intelligence, personality, and occupationally relevant attributes. Topics covered include: the context of testing and measurement; the testing process; test standardization; reliability and validity; intelligence and its appraisal; personality assessment; special domain testing; occupational applications; large-scale measurements; ethics and prospects.

This course introduces computer programming in Python. Students learn modern programming concepts, problem solving and creation of computer applications using the Python programming language. Topics include basic Python language syntax, control flow, functions, lambda expressions, Python's common data structures, list comprehensions, file I/O and operating system interface, object-oriented programming, functional programming, and basic usage of common data science packages such as NumPy and Pandas.