CS33115 Neural Computing
Introductory description
This module provides an introduction to the theory and implementation of neural networks and an understanding of the important computational neural network architecture and methodology. It aims to give students sufficient knowledge to enable employment or postgraduate study involving neural networks.
Module aims
This module provides an introduction to the theory and implementation of neural networks, both biological and artificial. It aims to give students sufficient knowledge to enable employment or postgraduate study involving neural networks.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Introduction: history of neural computing; relationship to Artificial Intelligence.
Neurons: structure and behaviour of biological neurons; simple models of neurons; nonlinear and
dynamical models.
Networks of Neurons: how neuronal networks are arranged in the brain; common architectures
for artificial networks.
Coding and Representation: how information is represented in neural networks; place coding; distributed representations.
Learning and Memory: plasticity in biological neurons; theories of memory; learning in artificial
networks.
Vision: structure of the human visual system; function of the retina, LGN and cortical processing;
artificial network models for vision.
Learning outcomes
By the end of the module, students should be able to:
 Students completing the module should be able to demonstrate: an understanding of the principles of Neural Networks and a knowledge of their main areas of application;
 the ability to design, implement and analyse the behaviour of simple neural networks.
Indicative reading list
Haykin S, Neural Networks: a Comprehensive Foundation, Macmillan, 2009.
Schalkoff R J, Artificial Neural Networks, New York, McGrawHill, 1997.
Subject specific skills
tbc
Transferable skills
tbc
Study time
Type  Required 

Lectures  30 sessions of 1 hour (20%) 
Seminars  5 sessions of 1 hour (3%) 
Practical classes  5 sessions of 1 hour (3%) 
Private study  110 hours (73%) 
Total  150 hours 
Private study description
Private study, background reading and revision
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group D1
Weighting  Study time  

assignment  20%  
Online Examination  80%  
Examination

Assessment group R
Weighting  Study time  

Online Examination  Resit  100%  
Examination

Feedback on assessment
Individual written feedback on coursework
Courses
This module is Optional for:

UCSAG4G1 Undergraduate Discrete Mathematics
 Year 3 of G4G1 Discrete Mathematics
 Year 3 of G4G1 Discrete Mathematics
 Year 3 of UCSAG4G3 Undergraduate Discrete Mathematics
 Year 4 of UCSAG4G2 Undergraduate Discrete Mathematics with Intercalated Year

USTAG1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)
 Year 3 of G1G3 Mathematics and Statistics (BSc MMathStat)
 Year 4 of G1G3 Mathematics and Statistics (BSc MMathStat)
 Year 4 of USTAG1G4 Undergraduate Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)
This module is Option list A for:
 Year 4 of UCSAG504 MEng Computer Science (with intercalated year)

UCSAG500 Undergraduate Computer Science
 Year 3 of G500 Computer Science
 Year 3 of G500 Computer Science

UCSAG502 Undergraduate Computer Science (with Intercalated Year)
 Year 4 of G502 Computer Science with Intercalated Year
 Year 4 of G502 Computer Science with Intercalated Year

UCSAG503 Undergraduate Computer Science MEng
 Year 3 of G500 Computer Science
 Year 3 of G503 Computer Science MEng
 Year 3 of G503 Computer Science MEng

USTAG302 Undergraduate Data Science
 Year 3 of G302 Data Science
 Year 3 of G302 Data Science
 Year 3 of USTAG304 Undergraduate Data Science (MSci)
 Year 4 of USTAG303 Undergraduate Data Science (with Intercalated Year)
This module is Option list B for:
 Year 3 of UCSAG406 Undergraduate Computer Systems Engineering
 Year 3 of UCSAG408 Undergraduate Computer Systems Engineering
 Year 4 of UCSAG407 Undergraduate Computer Systems Engineering (with Intercalated Year)
 Year 4 of UCSAG409 Undergraduate Computer Systems Engineering (with Intercalated Year)

USTAGG14 Undergraduate Mathematics and Statistics (BSc)
 Year 3 of GG14 Mathematics and Statistics
 Year 3 of GG14 Mathematics and Statistics
 Year 4 of USTAGG17 Undergraduate Mathematics and Statistics (with Intercalated Year)