·         MA402: Game Theory I*       

·         MA408: Discrete Mathematics and Complexity

·         MA409: Continuous-Time Optimisation

·         MA410: Information, Communication and Cryptography

·         MA411: Probability and Measure

·         MA412 Functional Analysis and its Applications

·         MA413 Games of Incomplete Information

·         MA414: Stochastic Analysis

·         MA418: Preferences, Optimal Portfolio Choice, and Equilibrium

·         MA419: Search Games

Papers 5+6. Courses to the value of two half-units from:

·         FM402 Financial Risk Analysis

·         FM441 Derivatives

·         FM442 Quantitative Methods for Finance and Risk Analysis

·         EC484 Econometric Analysis (full unit)

·         EC487 Advanced Microeconomics (full unit)

·         GV4A3 Social Choice Theory and Democracy - not running 2009/10

·         OR401 Techniques of Operational Research

·         OR406 Mathematical Programming: Theory and Algorithms

·         OR408 Combinatorial Optimisation

·         OR426 Computer Modelling: Applied Statistics and Simulation

·         OR428 Model Building in Mathematical Programming

·         ST409 Stochastic Processes

·         ST418 Non-Linear Dynamics and the Analysis of Real Time Series

·         ST422 Time Series

·         Any other course with the approval of the MSc Programme Director and the teacher responsible for the course.

Paper 7. MA498: Dissertation in Mathematics (full unit equivalent)

* 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.

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

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. Click here for more details

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For general information, please email MSc-AM@maths.lse.ac.uk  

For information about your application, please contact LSE's Graduate Admissions Office, or see the LSE Graduate Prospectus

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