CSMMA21-Mathematics and Statistics for Data Science

Module Provider: Computer Science
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Dr Fazil Baksh
Email: m.f.baksh@reading.ac.uk

Type of module:

Summary module description:

This module is a maths and stats primer module containing key mathematics and statistics concepts.

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

• understand and use appropriate mathematical notation and concepts;


• understand mathematical and statisticalÌýtechniques for data analytics;


• applyÌýappropriate mathematical and statisticalÌýtechniques for small-scale and well-defined data analytics and tasks.

Additional outcomes:

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.

• 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;


• 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.Ìý Computational optimisation techniques.


• Probability and Distribution Theory: • Introduction to combinatorics; conditional probability; independence; Bayes theorem; random variables; distributions; expectation; co-variance; estimation methods; mean square error; sums of random variables; approximation theorems.


• Basic statistical modelling: hypothesis testing; linear and non-linear regression; regularisation methods, spline regression; Analysis of variance (ANOVA); Linear discriminant analysis (LDA); Principal component analysis (PCA).

Brief description of teaching and learning methods:

The module comprisesÌýlectures introducing the topics with appropriate tutorial support for learning the material. Practical time is provided where students can use a mathematical/statistical computing package to practice and further develop their understanding of the material covered.ÌýÌý

Reading List: Relevant background reading:

Engineering Mathematics Through Applications (5th edition), Kuldeep Singh, Palgrave, ISBN: 0-3 33- 92224-7.

Mathematics for Engineers (3rd edition), Anthony Croft and Robert Davison, Pearson, ISBN: 978-0- 13-205156-9.

Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong, Cambridge University Press, ISBN: 978-1-10-847004-9.

Modern Engineering Mathematics (3rd edition), Glyn James, Prentice Hall., ISBN: 0-13-018319-9.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures	25
Practicals classes and workshops	15
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (independent)	20
Ìý Ìý Wider reading (directed)	20
Ìý Ìý Advance preparation for classes	30
Ìý Ìý Preparation for tutorials	20
Ìý Ìý Preparation of practical report	30
Ìý Ìý Essay preparation	30
Ìý Ìý Reflection	10
Ìý	Ìý	Ìý	Ìý
Total hours by term	200	0	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Set exercise	100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

One individual assignment.

Formative assessment methods:

Examples and computer-based practicals.

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy £Penalties for late submission for Postgraduate Flexible programmes£, which can be found here: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmissionpgflexible.pdf
The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 50% overall.

Reassessment arrangements:

One 3-hour examination paper in August/September.

Additional Costs (specified where applicable):

1) Required text books:ÌýÌý

2) Specialist equipment or materials:ÌýÌý

3) Specialist clothing, footwear or headgear:ÌýÌý

4) Printing and binding:ÌýÌý

5) Computers and devices with a particular specification:ÌýÌý

6) Travel, accommodation and subsistence:ÌýÌý

Last updated: 22 September 2022