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. 

Assessment: 65% coursework and 35% examination

​Course Name: Machine learning in trading and finance

Course Type: List-C-1

​IsCEF: TBC

Course Credit: 6

Course Description:

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

Assessment: 50% coursework and 50% examination

​Course Name: Topics in financial technology

Course Type: List-C-1

​IsCEF: TBC

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: modelling 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.

Assessment: 50% coursework and 50% examination

​Course Name: Software development for quantitative finance

Course Type: List-C-1

​IsCEF: TBC

Course Credit: 6

Course Description:

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.

Assessment: 40% coursework and 60% examination

​Course Name: Techniques in computational finance

Course Type: List-C-1

​IsCEF: TBC

Course Credit: 6

Course Description:

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 first 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, 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.

# Assessment: 50% coursework and 50% examination

# Pending for University approval

​Course Name: Deep learning

Course Type: List-C-1

​IsCEF: TBC

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.

Assessment: 50% coursework and 50% examination

​Course Name: Cluster and cloud computing

Course Type: List-C-1

​IsCEF: TBC

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).

Assessment: 50% coursework and 50% examination

​Course Name: Data mining

Course Type: List-C-1

​IsCEF: TBC

Course Credit: 6