Computer Science

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.

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.

Module Specific Skills and Knowledge:
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
Discipline Specific Skills and Knowledge:
4 understand the theoretical underpinnings and practice of computer science
Personal and Key Transferable/ Employment Skills and  Knowledge:
5 select and use appropriate tools for problems solving
6 Communicate effectively in writing

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.

None

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.

ELE – http://vle.exeter.ac.uk

Reading list for this module: