Aims and Fit of Module

This module aims to equip students with a comprehensive knowledge on the principles, techniques, algorithms and applications of Machine Learning (ML) and Data Mining (DM).
This module provides analysis techniques for today’s pervasive data, which are fundamental for business and financial analytics practitioners.

Learning outcomes

A. Demonstrate a basic and advanced knowledge of various Data Mining (DM) and Machine Learning (ML) algorithms from a practitioner/user perspective.
B. Critically assess the strengths and weaknesses of various DM and ML algorithms.
C. Apply DM and ML algorithms using a suitable (e.g. the Weka or R) platform.
D. Identify, formulate and solve problems arising from practical applications using DM and ML principles and techniques.

Method of teaching and learning

Students are expected to attend a two- hour formal lecture and either a two-hour tutorial or a two-hour lab session in a typical week. Lectures deliver the contents specified in the syllabus. Tutorials/labs expand students’ understanding of lecture materials and equip them with necessary programming skills.
In addition, students are expected to devote the required number of hours as unsupervised / private studies for reflection of lecture materials and reading and practical work. Two practical coursework assignments are used to assess their understanding of the lecture materials and their capability in solving practical problems. A written examination at the end of the module assesses their overall academic achievement.