MAS6019   Machine Learning and Time Series Analysis   (20 credits)

Description

The unit develops concepts and techniques for the analysis of data having the complex structure typical of many real applications. The two main themes are the analysis of observations on high-dimensional data, and the analysis of dependent observations made over a period of time on a single variable. Machine learning lies at the interface between computer science and statistics, whose aims are to develop a set of tools for modelling and understanding complex data sets. A review of repeated measures problems links to ideas of time series analysis. General techniques for the study of time series are developed, including structural descriptions, Box-Jenkins and state-space models and their fitting, techniques for forecasting and an introduction to spectral methods

Reading List