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STUDY PLAN & MODULE DESCRIPTIONS
Code Module Credits Duration Prerequisite(s)

Table I: Compulsory Modules
COMP6161 Smart Cities and Sustainability 3 45 hrs ---
This module describes a new design and planning approach to manage the impact of
smart city urban development by integrating those technological advances with living
systems and natural processes to enhance the health, livability, and equality in cities.
Smart Cities are complex challenges for governments because along with the benefits
come negatives such as uncontrolled development, traffic congestion, waste
management, complicated access to resources, and crime. Therefore, creation of
sustainable smart cities should be the main focus of following years and the
development of smart cities if they are to play a fundamental role in the models of
economic development. In these terms sustainable development of smart cities is
considered as for urban cities that could take advantage of all the possibilities that
Information and Communications Technologies (ICT) could offer to improve their
residents’ life quality, but always taking care of the environment, energy, waste
management, and sustainability of life.
COMP6162 Data Analytics 3 45 hrs ---
Recent advances in sensing technology and smart cities have led to the rapid explosion
of data. The ability to derive insights from big data is crucial for understanding complex
phenomena in various environmental contexts. This learning module provides an
overview of common data analytical techniques, including statistical inference and data
visualization. It also discusses implementation issues related to environmental data
analytics projects, including challenges in data collection, data cleaning and data
analysis. As part of a group project, students will need to demonstrate their ability to
analyze a given case study and relevant dataset, applying these techniques to address
specific questions related to urban environments.

COMP6163 Applied Machine Learning 3 45 hrs ---
Artificial Intelligence (AI) has become deeply integrated into our daily lives, often in ways
we may not even realize. At the forefront of AI is Machine Learning (ML), a branch of AI
that enables computers to learn and adapt without explicit programming. Over the past
decade, ML has revolutionized academics and industries with breakthroughs such as
autonomous vehicles, speech and image recognition, financial market analysis, machine
translation, and game strategies like AlphaGo. This module covers some of the key
machine learning techniques, including decision tree, neural networks, deep learning,
etc. The aim of the module is to equip students with both the theoretical foundation
and practical skills to apply these methods on environmental issues, such as
classification, regression, etc. Student will work on environmental data and help
stakeholders make informed decisions and take effective action about environments,
urban planning, and smart cities.

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