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> Artificial Intelligence and Neural Computing
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Artificial Intelligence and Neural Computing
Note:
Whilst every effort is made to keep the syllabus and assessment records correct
for this course, the precise details must be checked with the lecturer(s).
| Code: | GC26
(previously D26)
(Also taught as: 3058)
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| Year: | MSc |
| Prerequisites: | A strong background in university-level maths (in particular logic) |
| Term: | 2 |
| Taught By: | Denise Gorse (50%)
Anthony Hunter (50%)
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| 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.
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| 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.
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Content:
| 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
Resolution
Forward and backward chaining |
| Knowledge Representation | Diversity of knowledge
Inheritance hierarchies
Semantic networks
Knowledgebase ontologies |
| Handling uncertainty | Diversity of uncertainty
Inconsistency
Dempster-Shafer theory |
| Machine Learning | Induction of knowledge
Decision tree learning algorithms |
| Intelligent agents | An architecture for intelligent agents
Argumentation
Decision-making |
| 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.
Assessment:
The course has the following assessment components:
- Written Examination (2.5 hours, 90%)
- Coursework Section (2 pieces, 10%)
To pass this course, students must:
- Obtain an average of at least 50% when the coursework and exam components of a course are weighted together
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.Resources:
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
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