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Code Module Credits Duration Prerequisite(s)
YEAR 2 (Cont.)

SOCI1112 Sustainable Development 2 30 hrs ---
The learning module aims to enable students to understand global developments and
relevant trends as well as concepts of sustainable development in relation to promoting
economic growth, protecting the environment and addressing community needs in
education, health and social security. Students will be able to identify and recognize
factors contributing to sustainable development in the environmental, socio-economic
and other domains with a strong sense of global citizenship.
YEAR 3

--- Complete 1 subject from the elective 3 45 hrs ---
subjects – Group A
--- Complete 1 subject from the elective 3 45 hrs ---
subjects – Group B

COMP3111 Advanced Web Development 3 45 hrs COMP1122,
COMP2115
Recent advances in Web standards and their wide support by mainstream browsers have
enabled development of sophisticated Web applications that are accessible on desktop
and mobile devices. This course examines important concepts and technologies required
to develop state-of-the-art Web applications. Topics include the architecture and
protocol of the Web, the JavaScript language, development of interactive user interfaces
and scalable backend of Web applications, and the design and implementation of Web
APIs.
COMP3112 Project Management 3 45 hrs ---
The objective of this module is to study the concepts and issues related with
management of information technology projects. Topics include introduction to projects
and their management, project planning and development processes, project selection
methods, work breakdown structures, network diagrams & critical path analysis,
resource estimation, and project control, project organization structures, and various
project management models.
CSAI3121 Machine Learning and Intelligent 3 45 hrs MATH1111,
Data Analysis CSAI2121
This module will first provide an introduction to the most important concepts for
machine learning including different machine learning types, linear regression and
logistic regression, loss functions, gradient descents etc. The introduction of machine
learning and its applications will be taught with the Python Scikit-learn library. Students
will learn about the different types of machine learning algorithms, their applications,
and how to implement them using Scikit-learn. The module will cover data pre-
processing, model selection, evaluation, and tuning techniques. Some other important
machine learning algorithms are also covered, including: SVM, K Nearest Neighbours,
Game Theory, Genetic Algorithm etc. Students will learn these concepts with practices
There will be a group project for students to work on, students will work together for a
complete machine learning task involving problem analysis, data processing, model
selection and evaluation, solution design, system integration and final presentation.

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