5XÉçÇøÊÓƵ

School of Engineering and Informatics (for staff and students)

Neural Networks (G5015)

Neural Networks

Module G5015

Module details for 2021/22.

15 credits

FHEQ Level 6

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 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.

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 5XÉçÇøÊÓƵ Press.

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.

TypeTimingWeighting
Coursework100.00%
Coursework components. Weighted as shown below.
Problem SetA2 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.

TermMethodDurationWeek pattern
Spring SemesterLecture2 hours11111111111
Spring SemesterLaboratory1 hour11111111111

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

Please note that the 5XÉçÇøÊÓƵ will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the 5XÉçÇøÊÓƵ keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.

The 5XÉçÇøÊÓƵ reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the 5XÉçÇøÊÓƵ. If there are not sufficient student numbers to make a module viable, the 5XÉçÇøÊÓƵ reserves the right to cancel such a module. If the 5XÉçÇøÊÓƵ withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.

School of Engineering and Informatics (for staff and students)

School Office:
School of Engineering and Informatics, 5XÉçÇøÊÓƵ, Chichester 1 Room 002, Falmer, Brighton, BN1 9QJ
ei@sussex.ac.uk
T 01273 (67) 8195

School Office opening hours: School Office open Monday – Friday 09:00-15:00, phone lines open Monday-Friday 09:00-17:00
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