Neural Networks (G5015)

15 credits, Level 6

Spring teaching

In recent years, neural computing has emerged as a practical technology, with successful applications in many fields. The majority of these applications are concerned with problems in pattern recognition. It has become widely acknowledged that successful applications of neural networks require a more principled approach.

On this module, you will experience a more focused treatment of neural networks than previously available, which reflects these developments.

By concentrating on the pattern recognition aspects of neural networks, you will explore important topics like:

  • data pre-processing
  • probability density estimation
  • PCA/ICA and other information measures
  • multi-layer perceptron
  • radial basis function network
  • support vector machines
  • competitive learning
  • mixture of experts and committee machines
  • reinforcement learning.

You'll also learn how to apply neural networks to solving real world problems.


67%: Lecture
33%: Practical (Laboratory)


100%: Coursework (Problem set)

Contact hours and workload

This module is approximately 150 hours of work. This breaks down into about 44 hours of contact time and about 106 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.

We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2024/25. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum. We’ll make sure to let you know of any material changes to modules at the earliest opportunity.


This module is offered on the following courses: