Neural Networks (G5015)
Neural Networks
Module G5015
Module details for 2021/22.
15 credits
FHEQ Level 6
Library
1. Haykin S (1999). Neural networks. Prentice Hall International.
2. Bishop C (1995). Neural networks for pattern recognition. Oxford: Clarendon Press.
3. Duda RO, Hart PE and Stork DG (2001). Pattern Classification, John Wiley.
4. Ripley BD (1996). Pattern Recognition and Neural Networks. Cambridge University Press.
Module Outline
In recent years neural computing has emerged as a practical technology, with successful applications in many fields. The majority of these applications are concerned with problems in pattern recognition. Also, it has become widely acknowledged that successful applications of neural networks require a principled, rather than ad hoc, approach. The aim of this module is to provide a more focused treatment of neural networks than previously available, which reflects these developments. By deliberately concentrating on the pattern recognition aspects of neural networks, we shall treat many important topics such as data pre-processing, probability density estimation, PCA/ICA and other information measures, multi-layer perceptron, radial basis function network, support vector machines, competitive learning, mixture of experts and committee machines, reinforcement learning. Students will learn how to apply neural networks to solving real world problems.
Pre-Requisite
The course assumes an ability to write software in one appropriate programming language (e.g. Java, C, Python, Matlab). Basic knowledge of formal computational skills is also assumed.
Module learning outcomes
refer to relevant mathematical concepts to describe how modern, deep neural networks can be used as universal function approximators.
describe and critique the principles and applications of different neural network architectures.
describe and critique the principles underlying different design considerations and techniques used to optimise the performance of neural networks.
apply their knowledge of neural networks by building, optimising, and analysing a neural network for a real-world problem.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Problem Set | A2 Week 1 | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Lecture | 2 hours | 11111111111 |
| Spring Semester | Laboratory | 1 hour | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr James Bennett
Assess convenor
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Dr Temitayo Olugbade
Assess convenor
/profiles/272464
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