Programme And Module Handbook
Course Details in 2024/25 Session

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Module Title LH Neural Computation
SchoolComputer Science
Department Computer Science
Module Code 06 37082
Module Lead Per Kristian Lehre
Level Honours Level
Credits 20
Semester Semester 1
Restrictions None
Contact Hours Lecture-33 hours
Practical Classes and workshops-22 hours
Guided independent study-145 hours
Total: 200 hours
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 37082-01 : Examination : Exam (Centrally Timetabled) - Written Unseen (80%)
37082-02 : Continuous Assessment : Coursework (20%)
Assessment Methods & Exceptions Assessment: Examination (80%) and Continuous Assessment (20%)

Supplementary assessment: Examination (100%)
Other Dubai version of Birmingham based module 32167
Reading List