Course Description:

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.

Course Name: RegTech in finance

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

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)

Course Name: Network data analytics

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

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.

Course Name: Cluster and cloud computing

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

This course presents an industrial-strength approach to software development based on the 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; user interface 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.

Course Name: Analysis and design of enterprise applications in UML

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

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.

Course Name: Advanced database systems

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

This course will introduce selected advanced computational methods and apply them to problems in data analysis and relevant applications.

Course Name: Advanced topics in data science

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

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.

Course Name: Introduction to unmanned systems

Course Stream: Other Courses

IsCEF: No

Course Credit: 6

Course Description:

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.

Course Name: Big data management

Course Stream: Other Courses

IsCEF: No

Course Credit: 6