School of Computer Science

Module 32212 (2022)

Module description - Neural Computation (Extended)

The Module Description is a strict subset of the Syllabus Page.

Module Title Neural Computation (Extended)
School School of Computer Science
Module Code 06-32212
Level 4/M
Member of Staff Jinming Duan Konstantinos Kamnitsas Yunwen Lei
Semester Semester 1 - 20 credits

This course focuses on artificial neural networks and their use in machine learning. It covers the fundamental underlying theory, as well as methodologies for constructing modern deep neural networks, which nowadays have practical applications in a variety of industrial and research domains. The course also provides practical experience of designing and implementing a neural network for a real-world application.


On successful completion of this module, the student should be able to:

  • Describe and explain some of the principal architectures and learning algorithms of neural computation
  • Explain the learning and generalisation aspects of neural computation networks
  • Demonstrate an understanding of the benefits and limitations of neural networks in comparison to other machine learning methods.
  • Develop and apply neural network models to specific technical and scientific problems
  • Main Assessments: Examination (80%) and continuous assessment via coursework (20%)
  • Supplementary Assessments: Examination (100%)