Module Information

Module Identifier
Module Title
Fundamentals of Machine Learning
Academic Year
Semester 1
Reading List

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Exam 2 Hours   Written Exam  100%
Supplementary Exam 2 Hours   Written Exam  100%

Learning Outcomes

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

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

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

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

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

Brief description

The module provides an introduction to the fundamentals of machine learning and a number of different machine learning techniques and algorithms. It places emphasis on the practical applications of machine learning and highlights the theoretical advantages, drawbacks and limitations of the different techniques.


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
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 Inherent to machine learning
Communication Via written examination.
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 Via in-lecture problem-solving exercises. Real-world problems will be presented as part of the taught material.
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


This module is at CQFW Level 6