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6

90 hrs

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COMP6131 

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.

3

45 hrs

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COMP6132 

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.

3

45 hrs

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COMP6133 

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.

3

45 hrs

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COMP6134 

This learning module provides a comprehensive study of the major communication technologies and emerging standards that enable applications on Internet of Things (IoT). It covers a wide range of technologies which IoT is expected to bridge in the formation of an autonomous communication network that supports smart applications and intelligent decision making. Topics include: cellular technologies (2G/3G/4G/5G) and M2M communications, covering their transmission characteristics, physical layer technologies, medium access protocols, and routing protocols; WiFi; Bluetooth; Radio Frequency Identification (RFID); Near Field Communication (NFC); Wireless Sensor Networks; Wireless Personal Area Networks including IEEE 802.15.4 and ZigBee, and the Low Power networks such as SigFox and LoRa.

3

45 hrs

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COMP6135 

Recent advances in information and communication technologies (ICTs) have led to the rapid explosion of data. Business intelligence derived from big data can help firms to better understand market needs, develop new products and services, improve operational efficiency, and acquire competitive advantages. This learning module provides an overview of common big data applications and analytical techniques (e.g., sentiment analysis, decision tree, clustering, classification, etc.) in business and discusses some implementation issues related to big data projects. As part of a group project, students will need to demonstrate the ability to come up with a business plan based on a given case study and a relevant data set.

3

45 hrs

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COMP6298 

Students are required to apply the techniques and technologies which they have learned in a significant advanced project. Under the supervision of an advisor, the students shall focus on a contemporary technological problem and make use of the leading-edge techniques to produce new solutions. Upon completion, the project report is to be submitted and evaluated using the standard criteria for advanced project.

9

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COMP6111

This module introduces the principal algorithms for linear, network, discrete, stochastic, system and process optimizations. Emphasis is on methodology and the underlying mathematical structures. Topics include the calculus, LP (Linear Programming), simplex method, network flow, game theory, queueing theory, system engineering and process optimization.

3

45 hrs

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COMP6112

This module focuses on information systems security. Students will learn fundamentals of computer security, formal models of security, aspects of information systems security such as access control, hacks/attacks, systems and programs security, intrusion detection, cryptography, networks and distributed systems security, worms, and viruses, and other Internet secure applications. Students will develop the skills necessary to formulate and address the security needs of enterprise and personal environments.

3

45 hrs

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COMP6113

Cloud Computing is one important technological innovation, and being adopted across industries at a rapid pace. With improved data redundancy and availability across different geographical locations, Cloud Computing transforms the ways how services, applications, and solutions are delivered. With the rises of novel virtualization technologies and new programming paradigms, applications can be delivered quickly to customers, without the need to own any physical infrastructure. Furthermore, with its rapid elasticity and scalability, Cloud Computing offers low-cost solutions to the needs of companies of any sizes. It is the perfect operating platform for housing Big Data systems and analysing collected IoT sensing data. In this module, the main characteristics and enabling technologies of Cloud Computing, including orchestration of compute nodes, and different service paradigms, will be discussed. Other underpinning issues such as security, privacy, and ethical concerns are also covered.

3

45 hrs

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COMP6114

This learning module aims to provide students with the advanced topics of multimedia compression and communication, and the in-depth concepts and applications of computer vision. Topics include the principles of scalable video and audio codecs, file formats and codec settings for optimizing the quality and media bandwidth, applying the codecs in developing a basic media player application that is suitable for mobile access, in-depth concepts and methods of computer vision, and the structure of the applications of computer vision.

3

45 hrs

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COMP6115

This module will cover core concepts of probability theory and statistics with applications. Specific topics will include random variables and distributions, quantitative research methods (correlation and regression), and modern techniques of optimization and machine learning (clustering and prediction).

3

45 hrs

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COMP6116

The selected topics are designed to accommodate new, advanced and state-of-the-art technologies that are not included in this curriculum. One example is data mining. Data Mining is one of the most popular research fields in Computer Science. The aim of this is to give an applicable understanding of the usage of data mining as of decision making. In this module, several essential fields would be discussed, including the classes of different algorithms and models, and the methodology of how to choose a suitable algorithm. Classification, pattern recognition and different learning types would be discussed and covered. Besides, other interdisciplinary topics, such as AI in drug discovery, can also be covered.

3

45 hrs

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COMP6117

The selected topics are designed to accommodate new, advanced and state-of-the-art technologies that are not included in this curriculum. One example is data mining. Data Mining is one of the most popular research fields in Computer Science. The aim of this is to give an applicable understanding of the usage of data mining as of decision making. In this module, several essential fields would be discussed, including the classes of different algorithms and models, and the methodology of how to choose a suitable algorithm. Classification, pattern recognition and different learning types would be discussed and covered. Besides, other interdisciplinary topics, such as AI in drug discovery, can also be covered.

3

45 hrs

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