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

Table I: Compulsory Modules
COMP6131 Internet of Things Essentials 3 45 hrs ---
This module provides a comprehensive overview of the Internet of Things (IoT) from the
global context, and introduces the design fundamentals of the IoT. An IoT environment
should facilitate interactions among intelligent machines, smart devices, ubiquitous
computers, physical objects and human users. A number of underlying technologies
enabling IoT will be discussed, for example, the sensing technologies, wireless sensor
networks, machine-to-machine communications, Cloud and Fog computing
technologies, etc. In particular, the core system architectures, such as the middleware to
design single device and multi-device systems, will be discussed. In order to obtain more
hands-on experience in building IoT applications, project-based system constructions
through interconnecting different smart sensing devices and programming Raspberry Pi
and Arduino single board computers will be covered.
COMP6132 Introduction to Big Data 3 45 hrs ---
This learning module covers the characteristics of Big Data, the sources of massive data
in enterprises and sensor networks, and the challenges in data preparation, data storage
and analytic processing. The students will acquire skills and working knowledge of the
Big Data tools and technologies. This course focuses on the planning, designing and
implementing Big Data solutions. Examples and exercises of Big Data systems are used
to provide hands-on experiences in the workings of major components in Big Data
solutions. The students will also be able to integrate the Big Data tools to form coherent
solutions for business problems. Finally, additional related topics in the area of Big Data,
such as alternative large-scale processing platforms, non-relational data stores, and
Cloud Computing execution infrastructure are presented.
COMP6133 Machine Learning 3 45 hrs ---
Artificial Intelligence (AI) is so pervasive today that possibly you are using it in one way
or the other and you don’t even know about it. One of the popular applications of AI is
Machine Learning (ML), which is the science of getting computers to learn without being
explicitly programmed. In the past decade, machine learning has given us many amazing
applications such as self-driving cars, speech recognition, image recognition, financial
trading, machine translation, AlphaGo etc. This module covers some of the most
important methods for machine learning including deep neural networks, reinforcement
learning, etc. The aim of the module is to give students the theoretical underpinnings of
machine learning techniques, and to allow them to apply such methods in a range of
areas such as image recognition, classification, automatic control etc. by practice.

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