Page contents > More About the Programme | MSc in Applicable Mathematics Degree Regulations | Further Information on Courses
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More About the Programme
All course modules are 'half-units' unless otherwise stated. This means that the teaching, (lectures, classes and tutorials), lasts for roughly 10 weeks per half-unit. In addition, you will undertake a project, equivalent to a full unit, in an appropriate branch of mathematics, and present your work in the form of a dissertation to be submitted by the 1st September.
Further details on each course can be obtained by clicking on the links below.
MSc in Applicable Mathematics Degree Regulations
Full year programme. Students are required to take courses to the value of four full units.
Paper 1
Papers 2+3+4
Choose three from:
Papers 5+6
Courses to the value of two half-units from:
Any other course with the approval of the MSc Programme Director and the teacher responsible for the course.
Paper 7
* MA402 will not be available to those who have already studied MA300/301 Game Theory, or who have studied this subject as part of an undergraduate degree.
Further Information on Courses
Below you will find outline information on individual courses in Mathematics within the programme for the MSc in Applicable Mathematics.
All the taught courses in Mathematics are "half-units", i.e., they typically run for just one of the two main teaching terms. In total, a student on the degree will take the equivalent of six taught half-units, including at least four in mathematics.
Algorithms and Computation
This is the compulsory core course for the degree, comprising an introduction to programming, data structures and the mathematics underlying the theory of algorithms.
The aim is to increase students' understanding of how to tackle a mathematical problem with the aid of a computer, and of what types of problem may need to be overcome.
The pre-requisite is a good general knowledge of mathematics, including familiarity with abstract concepts, and a willingness to cope with technical details of computer usage. No previous programming experience is required. The course is examined partly by projects, and partly by formal examination. Click here for more details|
Game Theory
This course studies game theory, the mathematical theory used to model situations of conflict and co-operation, and some of its applications in economics. Click here for more details|
Discrete Mathematics and Complexity
This course follows on from the core course Algorithms and Computation. The first part of the course covers some basic topics in Discrete Mathematics, with emphasis on algorithmic aspects, and on problems that can be solved "efficiently". The second part deals with the theory of computational complexity, and explores problems that (apparently) cannot be solved efficiently. Click here for more details|
Games of Incomplete Information
Mathematical Game Theory has progressed rapidly in the last three decades. The techniques and results of game theory are increasingly important to economic analysis. This course is designed as an introduction to two branches of mathematical game theory: the theory of asymmetric information in repeated games, and Bayesian games. This is a relatively new but rapidly expanding area of game theory with connections to several areas of economic theory, for example conflict resolution, auctions, principal-agent problems, and the logic of knowledge. Click here for more details|
Probability and Measure
This course explains and develops the formal basis of abstract probability theory, and justifies the main results. It goes on to explore aspects of the theory that are most used in advanced analytical models in economics and finance (e.g., martingales, stochastic processes). Click here for more details|
Continuous-Time Optimisation
This is a course in Optimisation Theory using the methods of the Calculus of Variations. No specific knowledge (e.g., of functional analysis) will be assumed, and the emphasis will be on examples. Key methods of continuous-time optimisation are introduced first in a deterministic context, and then in the presence of uncertainty. Click here for more details|
Computational Learning Theory and Neural Networks
This course provides an introduction to artificial neural networks and other machine learning systems, using mathematical techniques (including probability, discrete mathematics and computational complexity) to analyse their power and the limits to their effectiveness. Click here for more details|
Information, Communication and Cryptography
This course provides an introduction to the applications of discrete mathematics and probability theory in information theory, coding theory, cryptography, and related areas. Click here for more details|
Functional Analysis and its Applications
Functional analysis plays an important role in the applied sciences as well as in mathematics itself. This course aims at familiarizing the student with the basic concepts, principles and methods of functional analysis and its applications. Methods of functional analysis find wide applicability in diverse problems arising in the applied sciences. Click here for more details|
Dissertation
At the end of the course, students carry out a substantial project. This involves writing a report on an area of mathematical research, or on an application of advanced mathematical techniques. The dissertation topic will normally be proposed by the Department, but will be fitted, as far as possible, to the interests of the individual student.
Advice on preparing the dissertation is provided by an appointed supervisor. The bulk of the work is normally carried out after the end of the examinations (mid-June); the report is due by September 1st.
