Neural Networks (G5015)

15 credits, Level 6

Spring teaching

To take this module you must already be able to write software in one appropriate programming language such as Java, C, Python, or Matlab. Basic knowledge of formal computational skills is also a prerequisite.

In recent years neural computing has emerged as a practical technology with applications in many fields. The majority of these applications are concerned with problems in pattern recognition, and make use of feed-­forward network architectures such as the multi­layer perceptron and the radial basis function network.

It is widely acknowledged that the successful application of neural computing requires a principled approach, and this module will use the recent advances in neural computing to explore neural networks in-depth. By concentrating on the pattern-recognition aspects of neural networks, the module will cover many important topics such as:

  • spiking neural networks
  • multi­layer perception
  • radial basis function network
  • support vector machines
  • competitive learning
  • independent component analysis.

You will also learn to use neural networks in 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 2023/24. 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: