CSMMA16-Mathematics and Statistics
Module Provider: School of Mathematical, Physical and Computational Sciences
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded: SEMMA13 Engineering Mathematics and Statistics
Module version for: 2016/7
Module Convenor: Dr Giuseppe Di Fatta
Email: g.difatta@reading.ac.uk
Summary module description:
This module is a maths and stats primer module containing key mathematics and statistics concepts for Computer Science MSc programmes.
Aims:
The module aims to bring students up to the appropriate level as regards the mathematics and statistics necessary for the modules taught as part of the MSc in Advanced Computer Science. It contains a number of topics and students will focus on those they have not met before and which are most relevant to their degree.
Assessable learning outcomes:
Students will be able to perform appropriate mathematical techniques, which they will then be able to use in other modules.
Outline content:
The module covers the topics of calculus, vectors and matrices, , Probability and Statistical Modelling. It also includes an introduction to a mathematical/statistical computing package.
Introductory Lecture – setting scene.
Matrices and Vectors : basic operations; linear independence; rank of a matrix; determinants and inverses; linear systems of equations; eigenvalues and eigenvectors; positive definite and negative definite matrices; dot and cross products; singular values, vector and matrix norms. linear vector spaces; open, closed and compact sets.
Calculus – reminder of differentiation; integration; differential equations; numerical solution of ODEs; functions of several variables; vector functions; partial differentiation; gradient vector; Jacobian and Hessian matrices; Taylor series expansions; unconstrained optimisation of differentiable functions of several variables.
Probability: distributions; conditional probability; independence; statistics; mutual information, entropy, random variables, co-variance, independence.; partial fraction expansions; inverse transforms; convolution and correlation
Basic statistical modeling (linear and non-linear regression); Analysis of variance (ANOVA); Linear discriminant analysis (LDA); Principal component analysis (PCA).
Introduction to Combinatorics.
Brief description of teaching and learning methods:
The module comprises an initial lecture introducing the module with appropriate tutorial support for learning the material. Practical time is provided where students can use a mathematical/statistical computing package and practice their mathematical technique and answer the exercises.
Reading List:
Relevant background reading:
Engineering Mathematics Through Applications (5th edition), Kuldeep Singh, Palgrave, ISBN: 0-333-92224-7.
Mathematics for Engineers (3rd edition), Anthony Croft and Robert Davison, Pearson, ISBN: 978-0-13-205156-9.
Modern Engineering Mathematics (3rd edition), Glyn James, Prentice Hall., ISBN: 0-13-018319-9.
Summative Assessment Methods:
Method |
Percentage |
Set exercise |
100 |
Other information on summative assessment:
On line exercises
Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx
Length of examination:
N/A
Requirements for a pass:
50%
Reassessment arrangements:
By examination only, 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: 4 January 2017