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CS331 Neural Computing

CS331-15 Neural Computing

Academic year
20/21
Department
Computer Science
Level
Undergraduate Level 3
Module leader
Weiren Yu
Credit value
15
Module duration
10 weeks
Assessment
Multiple
Study location
University of Warwick main campus, Coventry
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, McGraw-Hill, 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%
2 hour online examination (Summer) 80%

Examination

Assessment group R
Weighting Study time
2 hour online resit examination (September) 100%

Examination

Feedback on assessment

Individual written feedback on coursework

Past exam papers for CS331

Courses

This module is Optional for:

  • Year 3 of UCSA-G4G1 Undergraduate Discrete Mathematics
  • Year 3 of UCSA-G4G3 Undergraduate Discrete Mathematics
  • Year 4 of UCSA-G4G2 Undergraduate Discrete Mathematics with Intercalated Year
  • USTA-G1G3 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 USTA-G1G4 Undergraduate Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)

This module is Option list A for:

  • Year 3 of UCSA-G400 BSc Computing Systems
  • Year 4 of UCSA-G401 BSc Computing Systems (Intercalated Year)
  • Year 4 of UCSA-G504 MEng Computer Science (with intercalated year)
  • Year 3 of UCSA-G402 MEng Computing Systems
  • Year 4 of UCSA-G403 MEng Computing Systems (Intercalated Year)
  • Year 3 of UCSA-G500 Undergraduate Computer Science
  • Year 4 of UCSA-G502 Undergraduate Computer Science (with Intercalated Year)
  • Year 3 of UCSA-G503 Undergraduate Computer Science MEng
  • Year 3 of USTA-G302 Undergraduate Data Science
  • Year 3 of USTA-G304 Undergraduate Data Science (MSci)
  • Year 4 of USTA-G303 Undergraduate Data Science (with Intercalated Year)

This module is Option list B for:

  • Year 3 of UCSA-G406 Undergraduate Computer Systems Engineering
  • Year 3 of UCSA-G408 Undergraduate Computer Systems Engineering
  • Year 4 of UCSA-G407 Undergraduate Computer Systems Engineering (with Intercalated Year)
  • Year 4 of UCSA-G409 Undergraduate Computer Systems Engineering (with Intercalated Year)
  • Year 3 of UCSA-GN51 Undergraduate Computer and Business Studies
  • Year 4 of UCSA-GN5A Undergraduate Computer and Business Studies (with Intercalated Year)
  • Year 3 of USTA-GG14 Undergraduate Mathematics and Statistics (BSc)
  • Year 4 of USTA-GG17 Undergraduate Mathematics and Statistics (with Intercalated Year)

Further Information

Term 2

15 CATS (7.5 ECTS)

Online Material

Updated Module Syllabus 2020/21