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Module Title
LM Neural Computation (Extended)
School
Computer Science
Department
Computer Science
Module Code
06 32212
Module Lead
Alexander Krull
Level
Masters Level
Credits
20
Semester
Semester 1
Pre-requisites
Co-requisites
Restrictions
May not be taken by students who have already completed or are currently registered for the LH non-extended version of this module.
Exclusions
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
