Mathematics

Learn more about the modules study abroad students can take at the School of Mathematics.

Module codeMA0212
LevelL5
SemesterSpring Semester
Credits10

A lecture-based module, open to all students with suitable grounding.  Vectors in geometry are lines with arrows (representing translations in space), added by the parallelogram law.  Vectors in algebra are anything that can be modelled by lines with arrows in geometry, obeying certain rules.  A vector space is all vectors that can be constructed from some given set of vectors using these rules; it has a ‘dimension’.  A low-dimension space can sit inside a high-dimension space as a subspace.  The process of modelling one vector space by another is performed by a ‘linear map’ ; it too obeys certain rules.  Pairs of vectors can be related like forces and distances in Physics, with a dot product representing work done, or a quadratic form representing stored energy.

The aim of linear algebra is to recognise when these models are possible, and to choose the coordinate system to make everything as simple as possible.

Assessment

  • Examination - spring semester: 100%
Module codeMA0213
LevelL5
SemesterAutumn Semester
Credits10

A group consists of a set and a binary operation which satisfy certain axioms. Many important classes of mathematical objects can be regarded as groups, some examples being certain symmetry transformations or permutations under the operation of composition, integers under the operation of addition, nonzero rational numbers under the operation of multiplication, and invertible real square matrices of fixed size under the operation of matrix multiplication.

Some of the basic definitions and concepts in group theory were introduced in the Year One module MA1005 Foundations of Mathematics I.  This Year Two module will provide a reinforcement and extension of material from Year One, together with the introduction and study of further important definitions, theorems, proofs and examples.

Assessment

  • Examination - autumn semester: 100%
Module codeMA0216
LevelL5
SemesterSpring Semester
Credits10

How can we describe the solutions in positive integers of x2 + y= z2 ?  This is “classical'' (there is a connection with Pythagoras' Theorem). Analogous questions include the solution of x2 + y= Kz2, with K = 2 or 3, for example. When K = 3 there are no solutions: this can be shown using congruence considerations and Fermat's notion of “infinite descent'', which is just mathematical induction expressed in a different way.

A different but related question is: which integers n (not necessarily squares) are representable as x2 + y2 , and, if they are, then in how many ways? An illuminating way of considering this question is via the study of Gaussian Integers x + iy, where i2 = - 1. This involves the study of Gaussian Primes and the uniqueness of factorisation of Gaussian Integers as products of Gaussian Primes.

Fermat's “Little'' Theorem (nothing to do with “Fermat's Last Theorem'') says that ap -a is divisible by p when p is prime. Historically, this result seems to have arisen from questions involving “perfect'' numbers n (whose factors sum to 2n), but the result has far greater significance elsewhere. It is needed, for example, in a characterisation of those primes p which divide numbers of the form n2+1 (they are precisely those p which are not of the form 4k+3), a fact which is indispensable for a study of Gaussian Primes and Sums of Two Squares.

Prerequisite Modules: MA0111 Elementary Number Theory I

Assessment

  • Examination - spring semester: 100%
Module codeMA0232
LevelL5
SemesterAutumn Semester
Credits10

This module considers the study of pairs of differential equations. Theoretical analysis, complemented by results obtained using computer simulation, will be used to study models drawn from a variety of disciplines.

Assessment

  • Examination - autumn semester: 100%
Module codeMA0235
LevelL5
SemesterSpring Semester
Credits10

The behaviour of fluid flows is important in a very wide variety of systems.  In weather and climate change studies it is necessary to predict and understand the general motion of both the air and the ocean. In medicine, it is important to know how blood flows in the arteries and the heart, and how air flows in the lungs. For instance, mathematically based simulations of the motion of blood in the heart have recently become sophisticated enough to guide surgeons when they take interventive action to treat various heart problems. In order to design aircraft it is necessary to know how wings can create a lifting force and how so-called viscous skin-friction can increase drag forces. If some means could be found to reduce skin-friction drag forces on aircraft by even just a few per cent, then this would translate into billions of pounds of savings in fuel costs for the airline industry every year.

The fundamental Euler and Navier-Stokes equations of fluid dynamics have been known for about two hundred and fifty years and a hundred and fifty years, respectively. Yet there remain many open and interesting questions about their solutions. This is despite the fact that, using a suitably compact notation, the equations are so short that they can each be written down in two lines.  The behaviour of turbulent flows, for example, can be described by solutions of these equations. Turbulent flows are ubiquitous in the natural world, as well as in engineered systems.  But no systematic means of obtaining turbulent solutions is known. Thus turbulence is an area of work that continues to attract the attention of many thousands of researchers, both in industry and over a range of academic departments within universities.

This module aims to provide students with a first look at the equations that govern the motion of fluids. We will extract a few simple solutions of these equations and discuss how they can be interpreted. To do this we will need to introduce various fundamental notions such as: particle paths; rates of change following the fluid flow (so-called material derivatives); mass and momentum conservation equations; and vorticity, which leads to an important distinction between two possible types of flow.

Prerequisite Modules: MA1300 Mechanics I

Corequisite Modules: MA2301 Vector Calculus

Assessment

  • Examination - spring semester: 100%
Module codeMA0261
LevelL5
SemesterSpring Semester
Credits20

Operational Research (OR) is the application of advanced analytical methods to help make better decisions.  Often this takes the form of developing a mathematical model of a system under consideration and then using the model to examine and quantify “What if?” type questions in order to improve its performance.

This double module provides an introduction to a number of topics in OR, viz Queueing Theory, Simulation, Linear Programming and Network Analysis.  These topics are orientated towards applications of mathematics in real-life situations.  This module is a prerequisite to certain Year Three modules in OR.

Prerequisite Modules: MA1500 Introduction to Probability Theory

Recommended Modules: MA1501 Statistical Inference

Assessment

  • Examination - spring semester: 90%
  • Written assessment: 10%
Module codeMA0276
LevelL5
SemesterSpring Semester
Credits10

This module assumes knowledge of the basic concepts of spreadsheets and how they can be used to manipulate information. It then builds on this to cover the automation of tasks using macros and the use of Visual Basic programming within Microsoft Excel, thus enabling the construction of customised, user-friendly interfaces for a spreadsheet. A variety of Operational Research problems are used as the basis for this module, although no prior knowledge of OR is required.

Topics covered include simulation, logical programming ideas, algorithm design and debugging. This module can be taken successfully by any student who is prepared to learn the basics of computer programming, and who wishes to learn some practical problem solving skills which may be of benefit in future employment.

Assessment

  • Written assessment: 40%
  • Written assessment: 60%
Module codeMA0291
LevelL5
SemesterSpring Semester
Credits10

To give an appreciation of the nature and significance of Accounting in the private sector of the economy by an examination of the contribution it can make to the internal administration and external financing of a firm.  This module also highlights the pivotal role of accounting as a service activity within a broad business context.

Assessment

  • Examination - spring semester: 100%
Module codeMA0322
LevelL6
SemesterAutumn Semester
Credits10

Knots are closed strings in three dimensional space. The fundamental question is to decide when two given knots are the same or if a particular knot is equivalent to another or even knotted at all. Knots have been studied by mathematicians for over a century but in the last 25 years a number of new simple ideas have contributed to remarkable breakthroughs which have helped clear up a large number of outstanding problems and conjectures. These ideas have come from a number of branches of mathematics and not only have influenced knot theory itself but have revolutionised several branches of mathematics and even mathematical physics. Applications have also been found in biology in understanding how DNA strands are knotted. This course is an elementary introduction to modern knot theory as it now stands and some of the tools which are now available for understanding knots. The style and emphasis is on using and understanding the tools rather than a traditional definition-theorem-proof approach.

Prerequisite Module: MA0212 Linear Algebra

Assessment

  • Examination - autumn semester: 100%
Module codeMA0332
LevelL6
SemesterSpring Semester
Credits10

A lecture based module which develops classical applied mathematical material introduced in Level Two modules and in Autumn Semester Level Three modules.

Prerequisite Modules: MA0235 Elementary Fluid Dynamics, MA2301 Vector Calculus

Assessment

  • Examination - spring semester: 100%
Module codeMA0367
LevelL6
SemesterSpring Semester
Credits10

This is a lecture based module designed to acquaint students with the principles of fitting time series models to data and with use of such model in forecasting.  The goals of this module are to develop an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing data. This module is aimed at the students who wish to gain a working knowledge of time series and forecasting methods as applied in economics, engineering and the natural and social sciences.

Prerequisite Modules: MA2500 Foundations of Probability and Statistics

Recommended Modules: MA3502 Regression Analysis and Experimental Design

Assessment

  • Examination - spring semester: 90%
  • Written assessment: 10%
Module codeMA0391
LevelL6
SemesterDouble Semester
Credits20

This double module provides an opportunity to undertake, with supervision, a relatively substantial piece of project work relevant to the student's scheme of study.

A wide range of projects will be offered to students. Some projects will require the student to engage in a detailed study of mathematical theories or techniques in an area of current interest. Other projects will be centred on specific problems that require the formulation of a mathematical model, its development and solution.

Assessment

  • Report: 85%
  • Presentation: 15%
Module codeMA0392
LevelL6
SemesterAutumn Semester
Credits10

This module provides an opportunity to undertake, with supervision, a piece of project work relevant to the student's scheme of study.

A range of projects will be offered to students. Some projects will require the student to engage in a study of mathematical theories or techniques in an area of current interest. Other projects will be centred on specific problems that require the formulation and development of a mathematical model.

Assessment

  • Report: 85%
  • Presentation: 15%
Module codeMA0392
LevelL6
SemesterSpring Semester
Credits10

This module provides an opportunity to undertake, with supervision, a piece of project work relevant to the student's scheme of study.

A range of projects will be offered to students. Some projects will require the student to engage in a study of mathematical theories or techniques in an area of current interest. Other projects will be centred on specific problems that require the formulation and development of a mathematical model.

Assessment

  • Report: 85%
  • Presentation: 15%
Module codeMA1001
LevelL4
SemesterSpring Semester
Credits10

The first part of the module aims to introduce students to first-order differential equations. Calculus techniques will be deployed to find simple solutions of such differential equations. In addition, students will be expected to develop an appreciation of how the solutions can be given a geometric interpretation, even when it is not possible to use calculus techniques to obtain solutions that can be written in a simple form.

The second part of the module is concerned with the solution of second-order differential equations. Manipulative techniques will be used to determine solutions of second-order differential equations for cases where the equation takes a specific and relatively simple form. There will also be some general discussion about the circumstances under which it is possible to know that there is a solution of a differential equation, even if a simple mathematical formula for the solution cannot be obtained.

 

Assessment

  • Examination - spring semester: 100%
Module codeMA1003
LevelL4
SemesterDouble Semester
Credits20

In the modern world it is imperative for a mathematician to know how to program. This module will give students an introduction to general concepts of programming that should empower them through their degree and beyond.

This module will introduce Students to programming through Python. The module will also teach particularities of programming applied to mathematics through Sage; an open source mathematics package built on Python.

Prerequiste:  A pass in A-level Mathematics of at least grade A.

Assessment

  • Class test: 40%
  • Written assessment: 30%
  • Presentation: 30%
Module codeMA1004
LevelL4
SemesterAutumn Semester
Credits10

This module gives an introduction to elementary plane Euclidean geometry. We present this material in a way which emphasises axiomatic approach, logical thinking and rigorous proofs, as well as careful use of diagrams as an aid to understanding problems and finding solutions. In the latter half of the module we also introduce basic notions of spherical geometry, emphasising the differences between it and Euclidean geometry.

Free Standing Module Requirements:  A pass in A-Level Mathematics of at least Grade A

Assessment

  • Examination - autumn semester: 100%
Module codeMA1006
LevelL4
SemesterSpring Semester
Credits20

In this module we will study rigorously real functions and their properties, focussing in particular on continuity and differentiability. We will give a mathematical definition of limits at a point, continuity, the derivative and the Riemann integral. We will show how to derive rigorously many of the computational rules already used at A-level.

Particular attention will be given to proving theorems for differentiable functions (as e.g. the Intermediate Value Theorem) and applications to maxima and minima, convexity and concavity. These tools can later be applied to qualitative study of functions and their graphs.

Later in the module we will introduce the Taylor expansion, which allows us to approximate most mathematical functions by polynomials. We will then study the general properties of the Riemann integral in detail, followed by the demonstration of the techniques of integration.

Free Standing Module Requirements:  A pass in A-Level Mathematics of at least Grade A

Precursor Modules: MA1005 Foundations of Mathematics I

Assessment

  • Examination - spring semester: 100%
Module codeMA1300
LevelL4
SemesterSpring Semester
Credits10

Classical continuum mechanics is a branch of mechanics, physics, and mathematics concerned with the behaviour of physical bodies which are either moving or at rest under the action of forces. This lecture based module focuses on basic continuum mechanics concepts and in particular on Newton's laws of dynamics, which are presented using modern mathematical tools and are applied to solve a number of mechanical problems taken from the physical world. The module is strongly recommended to all those who intend to pursue further study in applied mathematics, as well as to those interested in the roots of mathematics.

Free Standing Module Requirements:  A pass in A-Level Mathematics of at least Grade A

Assessment

  • Examination - spring semester: 100%
Module codeMA1500
LevelL4
SemesterAutumn Semester
Credits10

The module begins with the idea of a probability space, which is how we model the possible outcomes of a random experiment. Concepts such as statistical independence and conditional probability are introduced, and a number of practical problems are studied. We then turn our attention to random variables, and look at some well-known probability distributions. Following this we focus on discrete distributions, and introduce the idea of independence for random variables, and the important concept of mathematical expectation. This leads on to the study of random vectors, where we introduce covariance and correlation, conditional distributions and the law of total expectation. Finally, we show how the ideas developed for discrete distributions can be carried over to continuous distributions, and conclude with some approximation theorems.  

This is a lecture-based module. Students will be required to demonstrate problem-solving skills throughout the module. No previous knowledge of probability theory is assumed.

The module is intended to prepare students for subsequent modules involving probability and statistics within the degree scheme.

Free Standing Module Requirements:  A pass in A-Level Mathematics of at least Grade A

Assessment

  • Examination - autumn semester: 100%
Module codeMA1501
LevelL4
SemesterSpring Semester
Credits10

The role of statistics in the modern world is ever increasing and applications can be found in a wide variety of areas including science, industry, government and commerce making a basic understanding of statistics an essential skill.  This is a lecture based module given at an introductory level on statistical inference to develop an understanding of the basic principles of mathematical statistics, used in situations where the full picture of a problem (population) is unknown and must be inferred from collected data (random sample).

This module will be accessible to those who have knowledge of A-level Pure Mathematics and an Introduction to Probability Theory.  It will prepare students for all modules with statistics and probability content in future years of the degree scheme.

Free Standing Module Requirements:  A pass in A-Level Mathematics of at least Grade A

Precursor Module: MA1500 Introduction to Probability Theory

Assessment

  • Examination - spring semester: 100%
Module codeMA2001
LevelL5
SemesterAutumn Semester
Credits10

This module will be dedicated to transferring all basic notions of calculus of functions of one variable to functions of several variables including limits, continuity, differentiation and integration.

Assessment

  • Examination - autumn semester: 85%
  • Written assessment: 15%
Module codeMA2002
LevelL5
SemesterAutumn Semester
Credits10

A lecture based module, it provides an introduction to the manipulative parts of matrix algebra, and is essential for further work in all areas of mathematics.

Assessment

  • Examination - autumn semester: 100%
Module codeMA2003
LevelL5
SemesterSpring Semester
Credits10

A lecture based module, providing an exposition of the basic theory and methods of complex analysis which are fundamental in mathematics and many of its applications.

The course shows how the concepts of differentiation and integration of real functions can be extended to complex functions. Complex functions map complex numbers to complex numbers. For a special subset of these functions it is possible to define a derivative. These differentiable complex functions have particularly nice properties. The real integral between two points x1 and x2 on the real axis is generalised to a complex integral along a path between two points z1 and z2 in the complex plane. These integrals are called contour integrals. Theorems of Cauchy show how some contour integrals of differentiable complex functions can be evaluated in a beautiful and simple way using methods known as the residue calculus. The residue calculus can be used to evaluate real integrals.

This course is essential for all mathematics students.

Assessment

  • Examination - spring semester: 100%
Module codeMA2004
LevelL5
SemesterSpring Semester
Credits10

A lecture based module, which deals with fundamental mathematical methods which are essential to all students of mathematics or statistics. In particular the theory of certain important series and transforms is developed.

Assessment

  • Examination - spring semester: 100%
Module codeMA2005
LevelL5
SemesterSpring Semester
Credits10

Building upon a general understanding of the form and usefulness of ordinary differential equations and knowledge of elementary solution methods, this module explores the mathematical foundations of ordinary differential equation theory as well as methods for the asymptotic and qualitative study of their solutions.

It is an intriguing observation that only a very small number of types of differential equation can be solved in terms of the well-known elementary functions. Differential equations are therefore a fruitful source of new functions and thus are of great practical value in applications and remain of continuing interest. However, this also means that mere knowledge of techniques for the explicit solution of differential equations will not reach very far.

It is therefore essential to have a theoretical framework which ensures the existence of solutions of ordinary differential equations without the need to find them explicitly, and to study the uniqueness and continuous dependence of solutions on parameters of the equation. In the presence of singularities, the asymptotic behaviour of solutions is very valuable information. A further aspect of the qualitative study of ordinary differential equations is the question of stability: will nearby starting points lead to wildly different solutions (chaos), or will the solutions approach a fixed point or an attractive set of more complicated structure, e.g. a limit cycle?

The module will provide an introduction to the existence theory of ordinary differential equations and to fundamental techniques of the asymptotic and qualitative study of their solutions.

Assessment

  • Examination - spring semester: 100%
Module codeMA2300
LevelL5
SemesterAutumn Semester
Credits10

This module builds on the module Mechanics I (MA1300) by extending the study to general particle motion in 2 and 3 dimensions using vector methods. This is followed by studying systems of 2 and more particles leading to rigid bodies. Conservation principles are discussed and used. Finally, a brief introduction to Lagrangian mechanics is given.

Prerequisite Modules: MA1300 Mechanics I

Assessment

  • Examination - autumn semester: 100%
Module codeMA2301
LevelL5
SemesterSpring Semester
Credits10

The module extends the calculus of several variables (introduced in MA2001) to the description and analysis of vector and scalar fields. There will be an emphasis on ideas and results that can be applied in many areas of mathematical modelling. But the main vector calculus theorems that will be presented are also of significance without regard to any such applications. This is because they can be viewed as being natural extensions – to cases involving more than one-dimension - of the fundamental theorem of calculus which relates the process of integration to that of anti-differentiation.

Assessment

  • Examination - spring semester: 85%
  • Written assessment: 15%
Module codeMA2500
LevelL5
SemesterAutumn Semester
Credits20

Knowledge of probability and statistics is useful in many graduate careers. This double module gives students an understanding of the principles underlying statistical methods commonly used by professional statisticians, and is intended to prepare students for a career involving statistical analysis.

The first part of the module begins with the study of probability spaces, random variables and distributions, followed by the theory of mathematical expectation and conditional expectation. We then look at moment generating functions, which are used to prove classical limit theorems such as the law of large numbers and the central limit theorem. The second part of the module begins with a study of parameter estimation, including the notions of consistency and efficiency, and an introduction to Bayesian inference. We then look at the theory of statistical hypothesis testing, focusing in particular on the likelihood ratio test and a number of different non-parametric tests.

Prerequisite Module: MA1500 Introduction to Probability Theory

Recommended Module: MA1501 Statistical Inference

Assessment

  • Examination - autumn semester: 100%
Module codeMA3000
LevelL6
SemesterSpring Semester
Credits10

A lecture based module  covering some advanced topics in complex analysis. The module aims to cover topics which are of particular relevance to spectral theory, differential equations and special functions.

Prerequisite Modules: MA2003 Complex Analysis

Recommended Modules: MA0221 Analysis III

Assessment

  • Examination - spring semester: 100%
Module codeMA3003
LevelL6
SemesterAutumn Semester
Credits10

A group consists of a set and a binary operation which satisfy certain axioms, and a ring or field consists of a set and two binary operations which satisfy certain axioms.  Many important classes of mathematical objects can be regarded as groups, rings or fields.  For example, certain symmetry transformations or permutations under the operation of composition form a group, the integers under the operations of addition and multiplication form a ring, and the rational, real or complex numbers under the operations of addition and multiplication form fields.

Some fundamental definitions and results in group theory were studied in the Year Two module MA0213 Groups.  This Year Three module will provide a reinforcement and extension of material from Year Two, together with the introduction and study of important definitions, theorems and examples for rings and fields.  Students will thereby be exposed to a variety of the basic structures and concepts of abstract algebra.

Recommended Modules: MA0212 Linear Algebra, MA0213 Groups

Assessment

  • Examination - autumn semester: 100%
Module codeMA3004
LevelL6
SemesterSpring Semester
Credits10

Combinatorics is the branch of discrete mathematics concerned with the theory of arranging objects according to specified rules.  The objects can be material (such as people in a group or cards from a pack) or abstract (such as numbers, symbols, steps in a process or choices in a procedure). A frequent aim of combinatorics, when applied to particular cases, is to determine the number of arrangements, but without actually listing them. Accordingly, combinatorics is sometimes regarded as being the study of counting or enumeration. However, some other questions which can be addressed by combinatorics are whether certain arrangements are possible at all, and, if so, what an optimal way of obtaining them might be.  Also, in many cases the arrangements will depend on variables, and an aim is often then to study the number of arrangements as a function of these variables and, if possible, obtain an explicit formula for that counting function.

In this module, various general principles and methods of combinatorics will be studied, and then applied to several important enumeration problems, including some in graph theory. 

Assessment

  • Examination - spring semester: 100%
Module codeMA3005
LevelL6
SemesterSpring Semester
Credits20

The double module introduces students to some of the techniques of modern analysis which are indispensable tools to the present-day mathematician. The expansion of functions in Fourier series (if the function is defined on a bounded interval or periodic) or Fourier integrals is a very efficient method for solving a variety of problems in pure and applied mathematics – compared to power series expansion, it works under very weak assumptions on the regularity of the function. Indeed, even discontinuous functions can reasonably be expanded in a Fourier series, an observation which led to the modern definition of the concept of a function and to the development of mathematical analysis during the 19th and 20th centuries. The desire to give a satisfactory answer to the question which functions have a Fourier expansion, and in what sense, led to the abstract notions of normed vector spaces and Hilbert spaces, which have become the foundation of modern analysis and are used in all areas of mathematics. The fundamental idea is to try and extend the framework of linear algebra (matrix theory) to the study of more complicated linear operators, such as differential operators. This requires an infinite-dimensional setting, and ideas of analysis such as convergence and continuity become important. The aim of the course is to study Fourier series and integrals, with emphasis on conditions ensuring their pointwise, uniform or mean convergence, and to give an introduction to the more general theory of functional analysis, illustrated with some further applications.

Prerequisite Modules: MA0212 Linear Algebra, MA0221 Analysis III

Assessment

  • Examination - spring semester: 100%
Module codeMA3006
LevelL6
SemesterAutumn Semester
Credits20

This double module introduces the fundamentals of coding theory and data compression. 

The first part is devoted to coding theory and will mainly focus on error-correcting codes, their properties and applications. No document or computer files can be guaranteed free from error.  Error-correcting codes are used to spot mistakes and suggest the most likely correction. If the rate of errors is such that several mistakes are likely in a single ‘word’ (e.g. radio transmissions), then the codes used are more combinatoric.  If errors are so rare that having two mistakes in the same ‘word’ is very unlikely (e.g. brand new computer disc), then the codes used are more algebraic. Many error-correcting codes correspond to geometrical patterns.

The second part of the module deals with the broad field of data compression. We will first study lossless compression schemes, including the fundamental algorithms of Shannon, Huffman, Lempel-Ziv and arithmetic coding. Finally, the module will give the basic principles of lossy compression, such as quantization and transform coding. For instance, we will see the role wavelets (“the mathematical microscope”) play in data compression.

Assessment

  • Examination - autumn semester: 100%
Module codeMA3301
LevelL6
SemesterSpring Semester
Credits10

This module provides an introduction to nonlinear systems and their applications in modelling. The aims of the module are:

  • To introduce students to various aspects of the mathematical theory of nonlinear systems
  • To illustrate the use of nonlinear systems in mathematical modelling of various phenomena, particularly those that involve physical oscillations 
  • To describe the qualitative changes in the behaviour of solutions of nonlinear systems that can arise when a system parameter is varied 

Prerequisite Modules: MA0232 Modelling with Differential Equations

Assessment

  • Examination - spring semester: 100%
Module codeMA3303
LevelL6
SemesterAutumn Semester
Credits20

Partial differential equations are a central modelling tool in applied mathematics and mathematical physics. They also play an important role in pure mathematics, not least as a stimulus in the development of concepts and methods of classical and modern analysis.

This double module provides an introduction to the classical analytical treatment of second-order linear partial differential equations and techniques for their numerical solution. The essential concepts and methods are introduced and developed for prototype partial differential equations representing the three classes: parabolic; elliptic; hyperbolic. Finite difference and finite element approximations to the solutions of partial differential equations are developed. The accuracy and stability of the numerical schemes are investigated. Direct and iterative methods for solving the linear systems arising from the numerical approximation of partial differential equations are described.

Recommended Modules: MA0212 Linear Algebra, MA0232 Modelling with Differential Equations, MA2301 Vector Calculus

Assessment

  • Examination - autumn semester: 85%
  • Written assessment: 15%
Module codeMA3304
LevelL6
SemesterSpring Semester
Credits20

The purpose of this double module is to consolidate students’ knowledge of and skills in modelling, analysis and applications. The module therefore is situated on the interface between Pure and Applied Mathematics and encompasses three important themes relevant to investigating physical phenomena, which will be addressed in series.

Theme 1. Asymptotic Methods

Many mathematical problems contain a small or large parameter that may be exploited to produce approximations to integrals and solutions of differential equations, for example. This theme provides an introduction to asymptotic approximations and perturbation analysis and their applications. Such techniques are important in almost every branch of applied mathematics especially those where exact analytic solutions are not available and numerical solutions are difficult to obtain.

Theme 2. Integral Equations

Many mathematical problems, particularly in applied mathematics, can be formulated in two distinct but related ways, namely as differential equations or integral equations. In the integral equation approach the boundary conditions are incorporated within the formulation of the problem and this confers a valuable advantage to the approach. The integral approach leads naturally to the solution of the problem in terms of an infinite series, known as the Neumann expansion. Integral equations have played a significant role in the history of mathematics. The Laplace and Fourier transforms are examples of integral equations. Another interesting problem is Huygens’ tautochrone problem, which is a special case of Abel’s integral equation. This course is concerned for the most part with linear integral equations. This module will introduce different types of integral equations and develop methods for their analysis and solution.

Theme 3. Calculus of Variations

What is the shortest distance between two points on a surface?  What is the shape of maximum area for a given perimeter?  These are two questions of the many that can be answered using calculus of variations.  The central problem involves an integral containing an unknown function – for example the length of a curve can be expressed as an integral along that curve.  Calculus of variations provides techniques for investigating minima of such integral functionals, which usually represent some physically or geometrically meaningful quantity. One example of great importance in modern technology is the use of minimisation in studying complex patterns observed under some conditions in shape-memory alloys. The course will consider the classical ``indirect'' approach to minimisation problems, through finding solutions of some related differential equations. However, due to some inherent (and indeed physically relevant) limitations of this method, which will become evident during the course, one has to combine it with a ``direct’’ variational technique. The power of the direct method spreads far and wide across the modern applications of mathematics. In particular, it provides a key to various techniques for finding approximate solutions to differential equations. 

This module can be taken by any student who is prepared to solve some differential equations and manipulate integrals.  Although some of the problems studied are of a physical origin, these will be presented in a self-contained way and there are no applied mathematics pre-requisites.

Assessment

  • Examination - spring semester: 100%
Module codeMA3502
LevelL6
SemesterAutumn Semester
Credits20

Regression analysis is arguably the most widely used in practice statistical tool. Fundamentals of regression analysis are thus the must for every student who will be seeking a statistics-related job. In a similar vein, the methods and principles of designing experiments are extremely important and regularly used by practitioners in a variety of disciplines. All the theoretical discussions are accompanied with solving practical problems.

Prerequisite Modules: MA1501 Statistical Inference

Assessment

  • Examination - autumn semester: 100%
Module codeMA3503
LevelL6
SemesterSpring Semester
Credits20

Stochastic processes play a key role in analytical finance and insurance, and in financial engineering. This course presents the basic models of stochastic processes such as  Markov chains, Poisson processes and Brownian motion. It provides an application of stochastic processes in finance and insurance. These topics are oriented towards applications of stochastic models in real-life situations.

Prerequisite Modules: MA2500 Foundations of Probability and Statistics

Assessment

  • Examination - spring semester: 90%
  • Written assessment: 10%
Module codeMA3700
LevelL6
SemesterSpring Semester
Credits10

Recent tremendous technical advances in processing power, storage capacity, and inter-connectivity of computer technology are creating unprecedented quantities of digital data. Data mining (also known as Knowledge Discovery in Data, or KDD), the science of extracting useful knowledge from such huge data repositories, has emerged as a young and interdisciplinary field. Data mining techniques have been widely applied to problems in industry, science, engineering and government, and it is widely believed that data mining will have profound impact on our society.

This module provides an introduction to the basic ideas and methods of mathematical data mining. In this course, we will consider the following problems: classification, cluster and outlier analysis, mining time-series and sequence data, text mining and web mining, pattern analysis.

A lecture-based module open to all students with a suitable grounding. It covers the fundamental data mining ideas (clustering, support vector machine analysis, semi-supervised learning, information retrieval, collaborative filtering, harmonic analysis) and the most important algorithms (the k-means algorithm,  support vector machines,  PageRank algorithm, k-nearest neighbour classification, Naive Bayes).

Assessment

  • Examination - spring semester: 100%
Module codeMA3900
LevelL6
SemesterAutumn Semester
Credits20

Mi fydd y modiwl yma yn darparu cyflwyniad i fyfyrwyr israddedig i addysgu mathemateg mewn ysgol uwchradd trwy gyfrwng y Gymraeg. Mi fydd myfyrwyr yn datblygu eu dealltwriaeth o addysg mathemateg ac o strwythur y cwricwlwm mathemateg ar lefel cyflwyniadol (israddedig).

Assessment

  • Written assessment: 25%
  • Written assessment: 15%
  • Presentation: 20%
  • Written assessment: 40%