Subject to the University’s approval
MSc(CompSc)-1
SYLLABUSES FOR THE DEGREE OF
MASTER OF SCIENCE IN COMPUTER SCIENCE
[This syllabus is applicable to students admitted to the curriculum in the academic year 2021-22 and
thereafter.]
Definition and Terminology
Stream of study – a specialisation in the curriculum selected by a candidate which can be General,
Cyber Security, Financial Computing and Multimedia Computing.
Discipline course – any course on a list of courses in the discipline of curriculum which a candidate
must pass at least a certain number of credits as specified in the Regulations.
Subject group – a subset of courses in the list of discipline courses which have the same specialisation.
Stream specific course – any course in a subject group which corresponds to the specialisation of the
stream of study.
Elective course – any Taught Postgraduate level course offered by the Departments of the Faculty of
Engineering for the fulfilment of the curriculum requirements of the degree of MSc in Computer
Science that are not classified as discipline courses.
Capstone Experience – a 12-credit project or a 24-credit dissertation which is a compulsory and integral
part of the curriculum.
Curriculum Structure
Candidates are required to complete 72 credits of courses as set out below, normally over one academic
year of full-time study or two academic years of part-time study:
Enrolment Mode of
10 courses + Project
Enrolment Mode of
8 courses + Dissertation
General Stream Cyber Security / General Stream Cyber Security /
Financial
Computing /
Financial
Computing /
Multimedia
Multimedia
Computing Stream
Computing Stream
Course Category
No. of Credits
No. of Credits
Discipline Courses Not less than 48 Not less than 48 Not less than 36 Not less than 36
[Include at least 24
credits in Stream
Specific Courses in
the candidate’s
[Include at least 24
credits in Stream
Specific Courses
in the candidate’s
corresponding
corresponding
stream of study]
stream of study]
Elective Courses
Capstone
Not more than 12
Not more than 12
12
24
Experience
Total
72
72
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Enrolment Mode
Candidates are required to successfully complete 72 credits to graduate. They can do that by studying
in one of the following enrolment modes:
(a) 10 courses (each equivalent to 6 credits) + Project (equivalent to 12 credits)
OR
(b) 8 courses (each equivalent to 6 credits) + Dissertation (equivalent to 24 credits)
Course Selection
Candidates shall select courses in accordance with the regulations of the degree. For General Stream,
candidate can choose any discipline courses listed below in any subject group, and undertake a
dissertation or a project (COMP7704 or COMP7705) in any area in computer science. In addition, to
qualify as a graduate of Cyber Security, Financial Computing or Multimedia Computing Stream,
candidates must pass at least 4 stream specific courses (at least 24 credits in total) in the corresponding
subject group, and undertake a dissertation or a project (COMP7704 or COMP7705) in the area of the
corresponding stream.
A. Cyber Security
COMP7806. Topic in information security
COMP7901. Legal protection of digital property
COMP7903. Digital investigation and forensics
COMP7904. Information security: attacks and defense
COMP7905. Reverse engineering and malware analysis
COMP7906. Introduction to cyber security
FITE7410. Financial fraud analytics
B. Financial Computing
COMP7103. Data mining
COMP7408. Distributed ledger and blockchain technology
COMP7409. Machine learning in trading and finance
COMP7412. Banking in Web 3.0 – Metaverse, DeFi, NFTs and beyond
COMP7415. Mastering the markets: Financial analytics and algorithmic trading
COMP7802. Introduction to financial computing
COMP7808. Topic in financial computing
COMP7906. Introduction to cyber security
FITE7405. Techniques in computational finance
FITE7406. Software development for quantitative finance
FITE7407. Securities transaction banking
FITE7410. Financial fraud analytics
FITE7413. Smart banking and innovative finance
FITE7414. Generative AI in financial services
C. Multimedia Computing
COMP7502. Image processing and computer vision
COMP7503. Multimedia technologies
COMP7504. Pattern recognition and applications
COMP7505. User interface design and development
COMP7506. Smart phone apps development
COMP7507. Visualization and visual analytics
COMP7508. Data-driven computer animation
COMP7604. Game design and development
COMP7807. Topic in multimedia computing
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D. Other discipline courses
COMP7104. Advanced database systems
COMP7105. Advanced topics in data science
COMP7106. Big data management
COMP7107. Management of complex data types
COMP7108. Network data analytics
COMP7201. Analysis and design of enterprise applications in UML
COMP7305. Cluster and cloud computing
COMP7308. Introduction to unmanned systems
COMP7309. Quantum computing and artificial intelligence
COMP7310. Artificial intelligence of things
COMP7311. Legal issues in artificial intelligence and data science
COMP7404. Computational intelligence and machine learning
COMP7602. Introduction to bioinformatics
COMP7607. Natural language processing
COMP7801. Topic in computer science
COPM7805. Topic in computer network and systems
COMP7809. Topic in artificial intelligence
DASC7606. Deep learning
FITE7411. RegTech in finance
Candidate may select no more than 2 courses (at most 12 credits in total) offered by other taught
postgraduate curricula in the Faculty of Engineering as electives. All course selection will be subject
to approval by the Programme Director and Course coordinators concerned.
MSc(CompSc) Course descriptions
The following is a list of discipline courses offered by the Department of Computer Science for the
MSc(CompSc) curriculum. The list below is not final and some courses may not be offered every year.
All courses are assessed through examination and / or coursework assessment, the weightings of which
are subject to approval by the Board of Examiners.
COMP7103. Data mining (6 credits)
Data mining is the automatic discovery of statistically interesting and potentially useful patterns from
large amounts of data. The goal of the course is to study the main methods used today for data mining
and on-line analytical processing. Topics include Data Mining Architecture; Data Preprocessing;
Mining Association Rules; Classification; Clustering; On-Line Analytical Processing (OLAP); Data
Mining Systems and Languages; Advanced Data Mining (Web, Spatial, and Temporal data).
COMP7104. Advanced database systems (6 credits)
The course will study some advanced topics and techniques in database systems, with a focus on the
aspects of database systems design & algorithms and big data processing for structured data. Traditional
topics include query optimization, physical database design, transaction management, crash recovery,
parallel databases. The course will also survey some the recent developments in selected areas such as
NoSQL databases and SQL-based big data management systems for relational (structured) data.
Prerequisites: A course of introduction to databases and basic programming skills.
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_________________________________________________________________________________
COMP7105. Advanced topics in data science (6 credits)
This course will introduce selected advanced computational methods and apply them to problems in
data analysis and relevant applications.
__________________________________________________________________________________
COMP7106. Big data management (6 credits)
The course will study some advanced topics and techniques in Big Data. It will also survey the recent
development and progress in specific areas in big data management and scalable data science. Topics
include but not limited to: large database management techniques, spatial data management and spatial
networks, data quality and uncertain databases, top-k queries, graph and text databases, and data
analytics.
Mutually exclusive with: COMP7107 Management of complex types
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________________________________________________________________________________
COMP7107. Management of complex data types (6 credits)
The course studies the management and analysis of data types which are not simple scalars. Such
complex data types include spatial data, multidimensional data, time-series data, temporal and spatio-
temporal data, sparse multidimensional vectors, set-valued data, strings and sequences, homogeneous
and heterogeneous graphs, knowledge-base graphs, geo-textual and geo-social data. For each of these
data types, we will learn popular queries and analysis tasks, as well as storage and indexing methods
for main memory and the disk.
Mutually exclusive with: COMP7106 Big data management
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______________________________________________________________________________
COMP7108. Network data analytics (6 credits)
In the era of data, numerous real-world applications are best represented as networks. This perspective
is vital as analyzing these networks can uncover valuable insights, extract interesting information, and
make informed decisions. Modern technologies have significantly enhanced our ability to access vast
volumes of data, simplifying and reducing the cost of storage. Understanding the importance of data is
crucial in addressing diverse challenges, such as traffic congestion, financial network fraud detection,
and the spread of misinformation in social networks, to name a few. Consequently, there is an increasing
necessity to develop advanced tools that can address these challenges and further understand the
importance of data is more necessary than ever. Examples of these technologies can be machine learning
techniques (e.g., modeling different problems using GNNs), and natural language processing (NLP)
techniques (text preprocessing and sentiment analysis).
Pre-requisites: Very good knowledge of programming (Python recommended) and knowledge of
fundamental data science concepts and techniques (e.g. linear algebra)
__________________________________________________________________________________
COMP7201. Analysis and design of enterprise applications in UML (6 credits)
This course presents an industrial-strength approach to software development based on object-oriented
modelling of business entities. Topics include: overview of software engineering and object-oriented
concepts; unified process and Unified Modelling Language (UML); use-case modelling and object
modelling; dynamic modelling using sequence diagrams and state machines; object-oriented design;
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modern web design; introducing design patterns and enterprise applications; shortcomings of UML and
remedies. Emphasis will be given on hands-on exercises with the use of CASE tools.
Prerequisites: A course in object-oriented programming and a course in software engineering or
systems analysis and design.
COMP7305. Cluster and cloud computing (6 credits)
This course offers an overview of current cloud technologies, and discusses various issues in the design
and implementation of cloud systems. Topics include cluster systems architecture and example
distributed/parallel programming paradigms; cloud delivery models (SaaS, PaaS, IaaS, and Serverless
Computing) with examples from popular public cloud platforms; virtualization techniques such as
hypervisor, virtual machines, and Docker; container orchestration and management tools, such as
Kubernetes; distributed programming models and systems such as MapReduce and Apache Spark; and
distributed file systems, such as Hadoop file system. Students will gain experience in setting up a
containerised environment using Kubernetes for running distributed applications (e.g., Web
applications, Spark applications) on public cloud environments (e.g., Amazon, Microsoft, Google,
Alibaba).
Prerequisites: Students are expected to perform installation and administration of various open-source
cloud/distributed software on their machines and the cloud. Basic understanding of Linux OS and
administration, networking concepts and setup, and programming experiences (C/C++, Java, or Python)
in a Linux environment are required.
COMP7308. Introduction to unmanned systems (6 credits)
To study the theory and algorithms in unmanned systems. Topics include vehicle modelling, vehicle
control, state estimation, perception and mapping, motion planning, and deep learning related
techniques.
COMP7309. Quantum computing and artificial intelligence (6 credits)
This course offers a theoretical overview of selected topics from the interdisciplinary fields of quantum
computation and quantum AI. The scope of the lectures encompasses an accessible introduction to the
fundamental concepts of quantum computation. Importantly, the introduction does not require
preliminary knowledge of quantum theory. Detailed comparisons of computational principles and
related phenomena in the classical and quantum domain outline the potential and challenges of quantum
theory for fundamentally novel algorithms with enhanced processing power. The theoretical capability
of quantum computers is illustrated by analyzing a selection of milestone algorithms of quantum
computation, and their potential applications to artificial intelligence and optimization.
COMP7310. Artificial intelligence of things (6 credits)
This course introduces basic concepts, technologies, and applications of the Internet of Things (IoT),
with a focus on smart sensing. The course features various topics on sensors and sensing techniques
that enable ubiquitous sensing intelligence for IoT devices, and connects them to exciting applications
in smart homes, healthcare, security, etc. The lectures introduce topics like localization, mobile sensing,
wireless sensing, acoustic sensing and their applications.
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COMP7311. Legal issues in artificial intelligence and data science (6 credits)
This course introduces students to the growing legal, ethical and policy issues associated with artificial
intelligence, data science and the related issues security and assurance. In particular, the relationship
of AI and data science to personal autonomy, information assurance and privacy are analyzed and
legislative responses studied. Class participation, research, writing, and oral/electronic presentations
are integral components of the course.
The course contributes to the following goals: written communication and life-long learning. It includes
coverage of the following goals: problem analysis, problem solving and teamwork.
COMP7404. Computational intelligence and machine learning (6 credits)
This course will teach a broad set of principles and tools that will provide the mathematical, algorithmic
and philosophical framework for tackling problems using Artificial Intelligence (AI) and Machine
Learning (ML). AI and ML are highly interdisciplinary fields with impact in different applications,
such as, biology, robotics, language, economics, and computer science. AI is the science and
engineering of making intelligent machines, especially intelligent computer programs, while ML refers
to the changes in systems that perform tasks associated with AI. Ethical issues in advanced AI and how
to prevent learning algorithms from acquiring morally undesirable biases will be covered.
Topics may include a subset of the following: problem solving by search, heuristic (informed) search,
constraint satisfaction, games, knowledge-based agents, supervised learning (e.g., regression and
support vector machine), unsupervised learning (e.g., clustering), dimension reduction, learning theory,
reinforcement learning, transfer learning, and adaptive control and ethical challenges of AI and ML.
Pre-requisites: Nil, but knowledge of data structures and algorithms, probability, linear algebra, and
programming would be an advantage.
COMP7408. Distributed ledger and blockchain technology (6 credits)
In this course, students will learn the key technical elements behind the blockchain (or in general, the
distributed ledger) technology and some advanced features, such as smart contracts, of the technology.
Variations, such as permissioned versus permissionless and private blockchains, and the available
blockchain platforms will be discussed.
Students will also learn the following issues: the security, efficiency, and the scalability of the
technology. Cyber-currency (e.g. Bitcoin) and other typical application examples in areas such as
finance will also be introduced.
Prerequisites: COMP7906 Introduction to cyber security or ICOM6045 Fundamentals of e-commerce
security and experience in programming is required.
Mutually exclusive with: FITE3011 Distributed Ledger and Blockchain
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COMP7409. Machine learning in trading and finance (6 credits)
The course introduces our students to the field of Machine Learning, and help them develop skills of
applying Machine Learning, or more precisely, applying supervised learning, unsupervised learning
and reinforcement learning to solve problems in Trading and Finance.
This course will cover the following topics. (1) Overview of Machine Learning and Artificial
Intelligence, (2) Supervised Learning, Unsupervised Learning and Reinforcement Learning, (3) Major
algorithms for Supervised Learning and Unsupervised Learning with applications to Trading and
Finance, (4) Basic algorithms for Reinforcement Learning with applications to optimal trading, asset
management, and portfolio optimization, (5) Advanced methods of Reinforcement Learning with
applications to high-frequency trading, cryptocurrency trading and peer-to-peer lending.
COMP7412. Banking in Web 3.0 – Metaverse, DeFi, NFTs and beyond (6 credits)
The course introduces students to new concepts of Banking with Web3.0 Technologies. Firstly, it will
review the evolution from traditional banking towards decentralized finance and token economies. It
will then assess the opportunities for new customer experiences with virtual reality and in the Metaverse
as well as examine the opportunities and risks of NFTs (non-fungible tokens). The course will
thoroughly examine the different types of Digital Assets, Digital Currencies and special forms like
Central Bank Digital Currencies (e-CNY, e-HKD). A critical factor in the evolution towards Web3-
Finance are the required regulations, a proper risk management and compliance of products and
processes. The course will elaborate on these with the help of case studies and contemporary scenarios
at the time of the lecture.
COMP7415. Mastering the markets: Financial analytics and algorithmic trading (6 credits)
Algorithmic trading is a trending investment approach nowadays that consists of identification of
trading opportunities and implementation via computer algorithms. This course will cover emerging
trend in the quantitative investment field, and introduce various data analysis techniques and
methodologies that are commonly employed in the industry.
The first half of the course focuses on financial data analysis and strategy implementation. The second
half of the course discusses practical techniques to manage and deploy algorithmic trading strategies in
real financial world.
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COMP7502. Image processing and computer vision (6 credits)
To study the theory and algorithms in image processing and computer vision. Topics include image
representation; image enhancement; image restoration; mathematical morphology; image compression;
scene understanding and motion analysis.
COMP7503. Multimedia technologies (6 credits)
This course presents fundamental concepts and emerging technologies for multimedia computing.
Students are expected to learn how to develop various kinds of media communication, presentation,
and manipulation techniques. At the end of course, students should acquire proper skill set to utilize,
integrate and synchronize different information and data from media sources for building
specific multimedia applications. Topics include media data acquisition methods and techniques;
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nature of perceptually encoded information; processing and manipulation of media data; multimedia
content organization and analysis; trending technologies for future multimedia computing.
COMP7504. Pattern recognition and applications (6 credits)
To study techniques in pattern recognition. Topics include statistical decision theory; density
estimation; dimension reduction; discriminant functions; unsupervised classification and clustering;
neural network; hidden Markov model; and selected applications in pattern recognition such as
characters and speech recognition.
COMP7505. User interface design and development (6 credits)
For technology products and services, the user experience is a major key to success. With advanced
development of processors, sensors, and new algorithms and software tools, more powerful and
expressive user interfaces can be implemented to improve human computer interaction and operation.
The course will study matching input and output devices with user capabilities, software and hardware
considerations, interface design methodologies, and future interface technologies. All of these topics
will be supported and demonstrated with current research and actual case studies.
COMP7506. Smart phone apps development (6 credits)
Smart phones have become an essential part of our everyday lives. The number of smart phone users
worldwide today surpasses six billion and is forecast to further grow by more than one billion in the
next few years. Smart phones play an important role in mobile communication and applications.
Smart phones are powerful as they support a wide range of applications (called apps). Most of the time,
smart phone users just download their favorite apps remotely from the app stores. There is a great
potential for software developer to reach worldwide users.
This course aims at introducing the design and technical issues of smart phone apps. For example,
smart phone screens are usually smaller than computer monitors while smart phones usually possess
more hardware sensors than conventional computers. We have to pay special attention to these aspects
in order to develop attractive and successful apps. Various modern smart phone apps development
environments and programming techniques (such as Java for Android phones and Swift for iPhones)
will also be introduced to facilitate students to develop their own apps.
Students should have basic programming knowledge.
Mutually exclusive with: COMP3330 Interactive Mobile Application Design and Programming
COMP7507. Visualization and visual analytics (6 credits)
This course introduces the basic principles and techniques in visualization and visual analytics, and
their applications. Topics include human visual perception; color; visualization techniques for spatial,
geospatial and multivariate data, graphs and networks; text and document visualization; scientific
visualization; interaction and visual analysis.
COMP7508. Data-driven computer animation (6 credits)
Basics of character animation, motion capture, inverse kinematics, physically based character
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animation, Basics of physically-based animation, rigid body dynamics, fluid simulation, hair
animation, cloth simulation, facial animation, crowd simulation, kinematography, performance
capture, skinning, data-driven character control, data-driven fluid animation, data-driven cloth
animation, data-driven facial animation, data-driven kinematography, data-driven skinning, data-driven
crowd animation, data-driven rendering, mesh-shape editing, data-driven mesh-shape editing
COMP7602. Introduction to bioinformatics (6 credits)
The course will focus primarily on human genomics and medical applications, but the
techniques will be broadly applicable across all species. The topics will include 1)
bioinformatics big data analytics and algorithms for sequence alignment and sequence
assembly, 2) bioinformatics tasks such variant identification and annotation, gene expression
and regulation, and 3) real-life bioinformatics applications such as personal genome analysis
and cancer genomics.
COMP7604. Game design and development (6 credits)
The course studies the basic concepts and techniques for digital game design and development. Topics
include: game history and genres, game design process, game production, 2D/3D graphics, physics,
audio/visual design, artificial intelligence.
Prerequisites: Basic programming skill, e.g. C++ or Java, is required
COMP7607. Natural language processing (6 credits)
Natural language processing (NLP) is the study of human language from a computational perspective.
The course will be focusing on machine learning and corpus-based methods and algorithms. We will
cover syntactic, semantic and discourse processing models. We will describe the use of these methods
and models in applications including syntactic parsing, information extraction, statistical machine
translation, dialogue systems, and summarization. This course starts with language models (LMs),
which are both front and center in natural language processing (NLP), and then introduces key machine
learning (ML) ideas that students should grasp (e.g. feature-based models, log-linear models and then
the neural models). We will land on modern generic meaning representation methods (e.g. BERT/GPT-
\3) and the idea of pretraining / finetuning.
COMP7704. Dissertation (24 credits)
Candidate will be required to carry out independent work on a major project that will culminate in the
writing of a dissertation.
COMP7705. Project (12 credits)
Candidate will be required to carry out independent work on a major project under the supervision of
individual staff member. A written report is required.
COMP7801. Topic in computer science (6 credits)
MSc(CompSc)-10
Selected topics that are of current interest will be discussed.
COMP7802. Introduction to financial computing (6 credits)
This course introduces the students to different aspects of financial computing in the investment banking
area. The topics include yield curve construction in practice, financial modelling and modern risk
management practice, etc. Financial engineering is an area of growing demand. The course is a
combination of financial product knowledge, financial mathematics and computational techniques.
This course will be suitable for students who want to pursue a career in this fast growing area.
Prerequisites: This course does not require any prior knowledge in the area of finance. Basic calculus
and numeric computational techniques are useful. Knowledge in Excel spreadsheet operations is
required to complete the assignments and final project.
COMP7805. Topic in computer network and systems (6 credits)
Selected topics in computer network and systems that are of current interest will be discussed.
COMP7806. Topic in information security (6 credits)
Selected topics in information security that are of current interest will be discussed.
COMP7807. Topic in multimedia computing (6 credits)
Selected topics in multimedia computing that are of current interest will be discussed.
COMP7808. Topic in financial computing (6 credits)
Selected topics in financial computing that are of current interest will be discussed.
COMP7809. Topic in artificial intelligence (6 credits)
Selected topics in artificial intelligence that are of current interest will be discussed.
COMP7901. Legal protection of digital property (6 credits)
This course introduces computer professionals to the various legal means of protecting digital property
including computer software, algorithms, and any work or innovation in digital form. Focus is on the
main issues in protecting digital property arising from developments in information technology, and
their legal solutions. Topics covered include, but are not limited to, the following: 1) Copyright
protection of software and websites, 2) Patent protection of software and algorithms, 3) Protection of
personal data.
Mutually exclusive with: COMP3311/CSIS0311 Legal aspects of computing and ECOM6004 Legal
aspects of IT and e-commerce
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COMP7903. Digital investigation and forensics (6 credits)
This course introduces the fundamental principles of digital investigation and forensics. The course
starts with a brief introduction to common computer crimes and digital evidence, and then moves on to
the computer basics and network basics pertaining to digital forensics, and finally comes to the
techniques for digital investigation and forensic examination.
COMP7904. Information security: attacks and defense (6 credits)
This is an ethical hacking course. In this course, we will teach students how to conduct ethical hacking
so as to better protect a computer system in a company. Topics include physical security, password
cracking, network hacking, operating system hacking, and application hacking. The course will also
discuss R&D problems related to hacking and defense. The course will try to strike a balance between
theory and practice so that students can understand the theories behind the hacking process as well as
get enough hands-on exercises to perform ethical hacking and defense.
Prerequisites: Students are expected to have knowledge in university level mathematics and systems
plus experience in programming.
COMP7905. Reverse engineering and malware analysis (6 credits)
This course provides students a foundational knowledge about reverse engineering and malware
analysis, through the study of various cases and hand-on analysis of malware samples. It covers
fundamental concepts in malware investigations so as to equip the students with enough background
knowledge in handling malicious software attacks. Various malware incidents will be covered, such as
cases in Ransomware, banking- Trojan, state-sponsored and APT attacks, cases in Stuxnet and malicious
software attacks on Industrial Control System and IoT devices. With the experience of studying these
cases and analyzing selected samples, the students will be able to understand the global cyber security
landscape and its future impact. Hands-on exercises and in-depth discussion will be provided to enable
students to acquire the required knowledge and skill set for defending and protecting an enterprise
network environment.
Students should have programming/development skills (Assembly, C, C++, Python) and knowledge in
Operating System and computer network.
COMP7906. Introduction to cyber security (6 credits)
The aim of the course is to introduce different methods of protecting information and data in the cyber
world, including the privacy issue. Topics include introduction to security; cyber attacks and threats;
cryptographic algorithms and applications; network security and infrastructure.
Mutually exclusive with: ICOM6045 Fundamentals of e-commerce security
DASC7606. Deep learning (6 credits)
Machine learning is a fast-growing field in computer science and deep learning is the cutting edge
technology that enables machines to learn from large-scale and complex datasets. Ethical implications
of deep learning and its applications will be covered and the course will focus on how deep neural
networks are applied to solve a wide range of problems in areas such as natural language processing,
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and image processing. Other applications such as financial predictions, game playing and robotics may
also be covered. Topics covered include linear and logistic regression, artificial neural networks and
how to train them, recurrent neural networks, convolutional neural networks, generative models, deep
reinforcement learning, and unsupervised feature learning.
Prerequisites: Basic programming skills, e.g., Python is required.
__________________________________________________________________________________
FITE7405. Techniques in computational finance (6 credits)
This course introduces the major computation problems in the field of financial derivatives and various
computational methods/techniques for solving these problems. The lectures start with a short
introduction on various financial derivative products, and then move to the derivation of the
mathematical models employed in the valuation of these products, and finally come to the solving
techniques for the models.
Pre-requisites: No prior finance knowledge is required. Students are assumed to have basic competence
in calculus and probability (up to the level of knowing the concepts of random variables, normal
distributions, etc.). Knowledge in at least one programming language is required for the
assignments/final project.
FITE7406. Software development for quantitative finance (6 credits)
This course introduces the tools and technologies widely used in industry for building applications for
Quantitative Finance. From analysis and design to development and implementation, this course covers:
modeling financial data and designing financial application using UML, a de facto industry standard
for object oriented design and development; applying design patterns in financial application; basic
skills on translating financial mathematics into spreadsheets using Microsoft Excel and VBA;
developing Excel C++ add-ins for financial computation.
Pre-requisites: This course assumes basic understanding of financial concepts covered in COMP7802.
Experience in C++/C programming is required.
FITE7407. Securities transaction banking (6 credits)
The course introduces the business and technology scenarios in the field of Transaction Banking for
financial markets. It balances the economic and financial considerations for products and markets with
the organizational and technological requirements to successfully implement a banking function in this
scenario. It is a crossover between studies of economics, finance and information technology, and
features the concepts from basics of the underlying financial products to the latest technology of
tokenization of assets on a Blockchain.
FITE7410. Financial fraud analytics (6 credits)
This course aims at introducing various analytics techniques to fight against financial fraud. These
analytics techniques include, descriptive analytics, predictive analytics, and social network learning.
Various data set will also be introduced, including labeled or unlabeled data sets, and social network
data set. Students learn the fraud patterns through applying the analytics techniques in financial frauds,
such as, insurance fraud, credit card fraud, etc.
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Key topics include: Handling of raw data sets for fraud detection; Applications of descriptive analytics,
predictive analytics and social network analytics to construct fraud detection models; Financial Fraud
Analytics challenges and issues when applied in business context.
Required to have basic knowledge about statistics concepts.
FITE7411. RegTech in finance (6 credits)
The course studies the use of regulatory technology, or RegTech, in the context of regulatory
monitoring, reporting and compliance. It demonstrates that the true potential of RegTech lies in its
ability to effect a profound transition from a Know Your Customer (KYC) to a Know Your Data (KYD)
approach, which relies on efficient processes for the collection, formatting and analysis of reported
data. The course covers the RegTech landscape and global challenges, the use of innovative
technologies and disruption, RegTech investment, application for authorized institutions and industry
adoption, illustrated with initiatives and examples in the Hong Kong context. It also discusses social
impact and regulation, and the future development of RegTech.
FITE7413. Smart banking and innovative finance (6 credits)
This course provides an in-depth exploration of blockchain technology and distributed ledger
technology (DLT) and their applications in the context of Smart Banking and Innovative Finance.
Students will gain a comprehensive understanding of the underlying principles, functionalities, and
potential benefits and challenges of the emerging Financial Technology (FinTech) 3.0.
The course will cover the emerging trend in Smart Banking and Innovative Finance with various
disruptive business-IT (DLT and BlockChain) models in the evolving FinTech ecosystem such as
decentralized finance (DeFi), central bank digital currencies (CBDC) and Hong Kong SAR
Government’s w-CBDC and rCBDC projects, eHKD/eCNY use cases, Open Banking and API
(Application Programming Interface) ecosystem, Virtual Banks and Stored Valued Facility (SVF),
Banking as a Service (BaaS), Banking as a Platform (BaaP), Faster Payment System (FPS) and cross-
border payment/forex applications, smart contracts, tokenization and tokenomics, WealthTech,
InsurTech, Self-Sovereign Identity (SSI), Zero Knowledge Proof (ZKP), and the related regulatory
considerations.
Through lectures, case studies, in-class discussions, group presentations and reflective exercises,
students will develop practical skills in designing, implementing, and managing blockchain and DLT
solutions for Smart Banking and Innovative Finance.
__________________________________________________________________________________
FITE7414. Generative AI in financial services (6 credits)
The course demonstrates ways of implementing Generative AI in various scenarios in a financial
institution. It examines regulatory and ethical requirements as well as the opportunities from harnessing
the conversational power of Generative AI for individualized content generation. We will examine how
to use GenAI to improve analytics and especially to augment human collaborators. A qualified outlook
into the future of the technology and its impact will conclude the course.
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