Programme And Module Handbook
Course Details in 2026/27 Session

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Module Title LM Neural Computation (Extended)
SchoolComputer Science
Department Computer Science
Module Code 06 32212
Module Lead Konstantinos Kamnitsas
Level Masters Level
Credits 20
Semester Semester 1
Restrictions May not be taken by students who have already completed or are currently registered for the LH non-extended version of this module.
Description This module introduces the basic concepts and techniques of neural computation, and its relation to automated learning in computing machines more generally. It covers the main types of formal neuron and their relation to neurobiology, showing how to construct large neural networks and study their learning and generalization abilities in the context of practical applications. It also provides practical experience of designing and implementing a neural network for a real world application.
Learning Outcomes By the end of the module students should be able to:
  • Understand the relationship between real brains and simple artificial neural network models
  • Describe and explain some of the principal architectures and learning algorithms of neural computation
  • Explain the learning and generalisation aspects of neural computation
  • Demonstrate an understanding of the benefits and limitations of neural-based learning techniques in context of other state-of-the-art methods of automated learning
  • Apply neural computation algorithms to specific technical and scientific problems
Assessment 32212-01 : Examination : Exam (Centrally Timetabled) - Written Unseen (80%)
32212-02 : Continuous Assessment : Coursework (20%)
Assessment Methods & Exceptions Assessment:
Examination (80%),
Continuous Assessment (20%)

Examination (100%)
Reading List