Module Information

Module Identifier
Module Title
Machine Learning
Academic Year
Semester 1
Exclusive (Any Acad Year)
Other Staff

Course Delivery



Due to Covid-19 students should refer to the module Blackboard pages for assessment details

Assessment Type Assessment length / details Proportion
Semester Assessment Practical Assignment  (3,000 words)  50%
Semester Exam 2 Hours   50%
Supplementary Assessment Practical Assignment  (3,000 words)  50%
Supplementary Exam 2 Hours   50%

Learning Outcomes

On successful completion of this module students should be able to:

Demonstrate a knowledge and understanding of the Machine Learning paradigm and the main approaches to machine learning.

Describe important, different machine learning techniques and algorithms and how they perform.

Select an appropriate Machine Learning technique and describe how this can be applied as a suitable solution for a given application problem or domain.

Compare and contrast the properties and limitations of different Machine Learning techniques and algorithms and discuss the implementational challenges involved in applying them.

Demonstrate a practical understanding of the use of Machine Learning techniques and algorithms by applying them to various domain problems.

Brief description

The module provides an introduction to machine learning and a number of different machine learning techniques and algorithms. It places significant emphasis on the practical elements and utilises practical sessions in order to implement the knowledge acquired through the delivered lecture material.


1. Introduction
Introduction to machine learning including example applications and classes of machine learning techniques

2. Decision Trees
Introduction to decision trees (classification trees and regression trees); over-fitting; pruning; application example

3. Bayesian Learning
Bayes' theorem/rule; maximum likelihood; maximum a posteriori; naive Bayes classifier; application example

4. Artificial Neural Networks (approx. 4 lectures)
Introduction to perceptrons, and perceptron-based artificial neural networks; linear separability; activation functions; back-propagation; application example

5. Support Vector Machines
maximum margin hyperplane; kernel trick; kernel functions; soft margins; application example

6.Selected Additional ML Topics
Introduction and discussion of another important machine learning technique topic, e.g., reinforcement learning, handling uncertainty, genetic programming

7. Summary and Revision

Module Skills

Skills Type Skills details
Application of Number
Communication Via written examination and report writing.
Improving own Learning and Performance Presents a general approach to machine learning. The principles can be adapted to any particular situation and are not specific to any domain.
Information Technology Use of computing to solve real-world problems
Personal Development and Career planning Real-world problems will be presented as part of the taught material and the assignments.
Problem solving Via in-lecture problem-solving exercises
Research skills Via in-lecture problem-solving exercises
Subject Specific Skills Demonstrate a knowledge and understanding of the Machine Learning paradigm and the main approaches to machine learning.
Team work Group work is not part of this module


This module is at CQFW Level 6