Artificial Intelligence and Machine Learning

Module Description

This module has three parts. The first part of the module will equip learners with the basic understanding of what AI is, its importance and impact on the financial industry as well as on business and society, and the numerous applications of AI in finance. You will also learn about various components of AI, including knowledge representation, reasoning, decision making and problem-solving. You will also be exposed to some of the AI tools such as Rapid miner, Weka and R.

The second part of the module will equip you with a basic understanding of what Machine Learning is, as well as delve into different types of learning and the hypothesis space. You will also explore various linear regression models and linear functions, as well as Bayes’ Theorem.

The third part of the module will take you through classification techniques such as K-Nearest Neighbours, Support Vector Machines and Linear SVM formulation. It also includes solving machine learning problems, and will touch upon neural networks and unsupervised learning.

Entry Requirements

Candidates who apply for this course must possess one of the following:

• a Level 6 degree in a related field;

OR

• a Level 5 diploma or higher diploma and 2 years’ work experience, preferably in the fintech

Preference is given to applicants having a Level 6 degree in Information Technology, Computer Science, Data Science, Statistics, Mathematics, Finance, Banking, Business Management, or any other area related to Financial Management. People with degrees related to IT will also be accepted if accompanied by solid experience in the financial sector.

Target Audience

This course is targeted at fintech professionals, including but not limited to:

• financial managers
• bank staff
• fintech employees
• accountants
• IT professionals with experience in finance

Career Paths

The programme aims to prepare you for middle management and senior managerial posts at any financial technology entity.  

How you’ll be assessed

The method of assessment is assignment-based.

The course comprises:

• 6 lectures per module;
• 3-hour lectures (evening);
• 12 hours of online content per module, comprising asynchronous online discussions, tutorials and/or videos.