CS2NC19-Neurocomputation

Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Spring term module
Pre-requisites: CS1PR16 Programming and CS1AC16 Applications of Computer Science or PY1SN Introduction to Systems Neuroscience
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2020/1

Module Convenor: Prof Richard Mitchell

Email: r.j.mitchell@reading.ac.uk

Type of module:

Summary module description:

This module covers the theory and implementation of a few types of artificial neural network. In

addition, one network is used as a case study for object-oriented programming. Students are

expected to implement a neural network and apply it to real world problems.

Aims:

The module aims to describe in detail a mode of computation inspired by such biological

functionality, namely artificial neural networks. The module also demonstrates how such a network

can be programmed using object orientation.

This module also encourages students to develop a set of professional skills, such as programming and research where they find a data set and then apply it to their neural net and write up as a conference paper.

Assessable learning outcomes:

To program a Multi Layer Perceptron (MLP) using the object-oriented paradigm

To find a suitable data set and investigate whether the MLP can learn the data

Additional outcomes:

Outline content:

Various neural network techniques are described, for some their implementation is provided, and suitable applications discussed. Networks and techniques examined include data processing;ÌýSingle and Multi- Layer Perceptrons and associated learning methods; Radial Basis Function networks, Weightless Neural Networks; Genetic Algorithms; Stochastic Diffusion Search andÌýKohonen networks.

Associated with the lectures is an assignment whereby students use the object-ori ented paradigmÌýto design and implement a neural network and then apply that network to a suitable problem.

Brief description of teaching and learning methods:

The module comprises 1 lecture per week, three lab practicals and an associated assignment.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures		10
Practicals classes and workshops		9
Guided independent study:		81
Ìý	Ìý	Ìý	Ìý
Total hours by term	0	100	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Set exercise	100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Part A - Program a MLP (30%) – this is achieved in three labs, where feedback is provided after each lab to help students to ensure they have a working program.

Part B - Apply the MLP (70%) – students find a suitable data set and perform suitable experiments using their MLP to determine whether the data can be learnt by their network

Formative assessment methods:

Penalties for late submission:

The Module Convenor will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day[1] (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 40% overall.

Reassessment arrangements:

One 2-hour examination paper in August/September.

Additional Costs (specified where applicable):

Last updated: 16 April 2020