• Course Description:

    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.

    Course Name: Introduction to cyber security

    Course Stream: Cyber Security, Financial Computing

    IsCEF: No

    Course Credit: 6

  • Course Description:

    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.

    Course Name: Machine Learning in Trading and Finance

    Course Stream: Financial Computing

    IsCEF: No

    Course Credit: 6

  • Course Description:

    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

    Course Name: Distributed ledger and blockchain technology

    Course Stream: Financial Computing

    IsCEF: Yes

    Course Credit: 6

  • Course Description:

    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).

    Course Name: Data mining

    Course Stream: Financial Computing

    IsCEF: No

    Course Credit: 6

  • Course Description:

    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.

    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.

    Course Name: Financial fraud analytics

    Course Stream: Cyber Security, Financial Computing

    IsCEF: No

    Course Credit: 6

  • Course Description:

    Selected topics in financial computing that are of current interest will be discussed.

    Course Name: Topic in financial computing

    Course Stream: Financial Computing

    IsCEF: No

    Course Credit: 6

  • Course Description:

    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.

    Course Name: Introduction to financial computing

    Course Stream: Financial Computing

    IsCEF: Yes

    Course Credit: 6

  • Course Description:

    # 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.

    # Subject to University approval

    Course Name: Securities transaction banking

    Course Stream: Financial Computing

    IsCEF: Yes

    Course Credit: 6

  • Course Description:

    # 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.

    # Subject to University approval

    Course Name: Software development for quantitative finance

    Course Stream: Financial Computing

    IsCEF: No

    Course Credit: 6

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