COMPGC26 - Artificial Intelligence and Neural Computing

This database contains 2016-17 versions of the syllabuses. For current versions please see here.

Code COMPGC26 (Also taught as: COMP3058)
Year MSc
Prerequisites Students should have either: (1) a degree in Mathematics; or (2) a degree in Philosophy in which they have completed and passed a formal logic module.
Term 2
Taught By Denise Gorse (50%)
Anthony Hunter (50%)
Aims This course introduces artificial intelligence and neural computing as both technical subjects and as fields of intellectual activity. The overall targets are: (1) to present basic methods of expressing knowledge in forms suitable for holding in computing systems, together with methods for deriving consequences from that knowledge by automated reasoning; (2) to present basic methods for learning knowledge; and (3) to introduce neural computing as an alternative knowledge acquisition/representation paradigm, to explain its basic principles and their relationship to neurobiological models, to describe a range of neural computing techniques and their application areas.
Learning Outcomes Ability to identify problems that can be expressed in terms of search problems or logic problems, and translate them into the appropriate form, and know how they could be addressed using an algorithmic approach. Ability to identify problems that can be expressed in terms of neural networks, and to select an appropriate learning methodology for the problem area.


Scope of the Subject
Nature and goals of AI.
Application areas.

Searching state-spaces
Use of states and transitions to model problems.
Breadth-first, depth-first and related types of search.
A* search algorithm.
Use of heuristics in search.

Reasoning in logic
Brief revision of propositional and predicate logic.
Different characterisations of reasoning.
Generalized modus ponens.
Forward and backward chaining.

Knowledge Representation
Diversity of knowledge.
Inheritance hierarchies.
Semantic networks.
Knowledgebase ontologies.

Handling uncertainty
Diversity of uncertainty.
Dempster-Shafer theory.

Machine Learning
Induction of knowledge.
Decision tree learning algorithms.

Intelligent agents
An architecture for intelligent agents.

Nature and Goals of Neural Computing
Comparison with rule-based AI.
Overview of network architectures and learning paradigms.

Binary Decision Neurons
The McCullough-Pitts model.
Single-layer perceptrons and their limitations.

The Multilayer Perceptron
The sigmoid output function.
Hidden units and feature detectors.
Training by error backpropagation.
The error surface and local minima.
Generalisation, how to avoid 'overtraining'.

The Hopfield Model
Content addressable memories and attractor nets.
Hopfield energy function.
Setting the weights.
Storage capacity.

Self-Organising Nets
Topographic maps in the brain.
The Kohonen self-organising feature map.

Method of Instruction:

Lecture presentations.


The course has the following assessment components:

  • Written Examination (2.5 hours, 90%)
  • Coursework Section (2 pieces, 10%)

To pass this module, students must:

  • Obtain an overall pass mark of 50% for all components combined.

The examination rubric is:
Answer three from six questions set, at least one from each of section A (AI) and section B (neural computing). All questions carry equal marks.


Artificial Intelligence - A Modern Approach; First Edition; Prentice Hall; ISBN: 0-13-103805-2


[Background reading] Neural Computing: An Introduction; R Beale and T Jackson; Institute of Physics Publishing; ISBN: 0-85-274262-2