Tools and Techniques

Module description

This module provides you with a basic knowledge of programming and of the mathematical tools and techniques necessary to pursue further modules within the area of Applied Artificial Intelligence. This knowledge will enable you to utilise the related literature and understand the theory and methods presented.

Full module specification

Module title:Tools and Techniques
Module code:ECMM406
Module level:M
Academic year:2013/4
Module lecturers:
Module credit:15
ECTS value:
Pre-requisites:
Co-requisites:
Duration of module: Duration (weeks) - term 1:

11

Duration (weeks) - term 2:

0

Duration (weeks) - term 3:

0

Module aims

The aim of the module is to ensure that you have a sound foundation in programming and mathematical skills to enable you to read scientific research papers and engage in quantitative research in computer science.

ILO: Module-specific skills

  • 1. develop knowledge and understanding of the principles and computational approaches for addressing applied computing problems.
  • 2. design, write and test programs written in Matlab
  • 3. apply ideas in linear algebra, calculus and probability

ILO: Discipline-specific skills

  • 4. understand the theoretical underpinnings and practice of computer science

ILO: Personal and key skills

  • 5. select and use appropriate tools for problems solving
  • 6. Communicate effectively in writing

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad
251250

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning & Teaching activities20Lectures
Scheduled Learning & Teaching activities5Workshops and surgeries
Guided independent study40Coursework
Guided independent study85Lecture & assessment preparation; private study

Summative assessment (% of credit)

CourseworkWritten examsPractical exams
10000

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Matlab assignment25Approx. 8 pages1,2,4,5Written
Linear algebra assignment25Approx. 8 pages1,3,4,5,6Written
Calculus assignment25Approx. 8 pages1,3,4,5,6Written
Probability assignment25Approx. 8 pages1,3,4,5,6Written

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
All aboveCoursework (100%)AllCompleted over the summer with a deadline last week of August

Re-assessment notes

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.
If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

Syllabus plan

1. Matlab:  Basic programming concepts;  variables,  control structures; procedural programming; I/O; structures, data visualisation.
2. Linear algebra: vectors;  combination of vectors;  scalar and vector products;  linear combinations, span, bases; matrices;  matrix combination; matrix-vector combination;  null space and rank; properties of orthogonal and symmetric matrices; solutions of systems of equations; determinants;  eigenvalues and eigenvectors; singular value decomposition.
3. Calculus: single variable differentiation and integration, and applications; partial differentiation, extrema and saddle points in several dimensions; Jacobians; multivariate integration; numerical methods for integration and differentiation.
4. Probability: sample spaces; probability as frequency and axioms; counting, permutations and combinations; independence and conditional probability; Bayes' rule; discrete distributions;  moments; probability density functions; common density functions.

Indicative learning resources - Basic reading

Author

Title

Edition

Publisher

Year

ISBN

McGregor C., Nimmo J. & Stothers W.

Fundamentals of University Mathematics

2nd

Horwood, Chichester

2000

000-1-898-56310-1

James, G

Modern Engineering Mathematics

4th with MyMathLab

Addison Wesley

2010

027373413x

Hamilton A.G.

Linear Algebra: an introduction with concurrent examples

 

Cambridge University Press

1989

000-0-521-32517-X

McColl, J

Probability

 

Arnold

1995

0000340614269

Stirzaker D.

Elementary probability

2

Cambridge University Press

2003

 

Module has an active ELE page?

Yes

Indicative learning resources - Web based and electronic resources

ELE – http://vle.exeter.ac.uk

Origin date

2012-11-19

Last revision date

2013-05-17