Measure Theory provides one of the key building blocks of Analysis, Probability Theory, and Ergodic Theory and is an indispensable tool in the theory of differential equations, Harmonic Analysis, Mathematical Physics and Mathematical Finance.

In this course we will develop a proper understanding of measurable functions, measures and the abstract Lebesgue integral. A special attention will be paid to applications of Measure Theory in the Probability Theory such as construction of probability spaces for random variables and stochastic processes, Laws of Large Numbers, the Central Limit theorem and their applications. The Radon-Nikodym Theorem will be applied to introduce a general definition of conditional expectation.

Pre-requisites: 24 units of level III mathematics or a degree in a numerate discipline or permission of the Head of Department.