CS2NN16-Neural Networks

Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Autumn / 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:
Module version for: 2017/8

Module Convenor: Prof Richard Mitchell

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

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.

Assessable learning outcomes:
By the end of the module the student should be able to apply various neural network techniques to 'real-world' problems; and to program a simple neural network using the object oriented paradigm.

Additional outcomes:

EA2 topics: Neural Network Programming and Academic Paper Writing.


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; Single and Multi- Layer Perceptrons and associated learning methods; Radial Basis Function networksm Weightless Neural Networks; Genetic Algorithms; Stochastic Diffusion Search.
Associated with the lectures is an assignment whereby students use the object oriented paradigm to 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:
  Autumn Spring Summer
Lectures 10
Practicals classes and workshops 9 0.5
Guided independent study 25 55.5
       
Total hours by term 44.00 56.00
       
Total hours for module 100.00

Summative Assessment Methods:
Method Percentage
Set exercise 100

Other information on summative assessment:

Formative assessment methods:

Penalties for late submission:
The Module Convenor will apply the following penalties for work submitted late, in accordance with the University policy.

  • where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the piece of work will be deducted from the mark for each working day (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: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
    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.

    Length of examination:

    None.


    Requirements for a pass:

    A mark of 40% overall.


    Reassessment arrangements:
    Examination only.
    One 2-hour examination paper in August/September.

    Additional Costs (specified where applicable):
    1) Required text books:
    2) Specialist equipment or materials:
    3) Specialist clothing, footwear or headgear:
    4) Printing and binding:
    5) Computers and devices with a particular specification:
    6) Travel, accommodation and subsistence:

    Last updated: 31 March 2017

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