Module Leader: Colm Connaughton (Mathematics, Complexity)

Taken by students from:

Context: This is part of of the Complexity DTC taught programme.

Module Aims:

This module aims to provide students with an introduction to basic techniques for data analysis, statistical modelling and inference, time-series forecasting and quantification of uncertainty.

Syllabus:

1. Introduction to probability and Bayes' rule, data and error bars, statistical models, time-series models and correlated data.
   
2. Statistical inference and fitting a model to data, Maximum Likelihood Estimation, curve fitting and linear regression, model selection, Bayesian inference and learning, statistical significance.
   
3. Spectral methods: Fourier series and applications, Discrete Fourier Transform, spectral analysis of time-series data, using the Fast Fourier Transform.
   
4. Supervised learning and clustering.
5. The module will include a group project which will challenge the students to apply the techniques covered in the lectures to make concrete predictions from particular data sets.

Illustrative Bibliography:

C.M. Bishop, Pattern Recognition and Machine Learning, Springer 2006

J.D. Hamilton, Time Series Analysis, Princeton University Press 1994

G.E.P. Box, G.M. Jenkins and G.C. Reisel, Time Series Analysis: Forecasting and Control, Prentice Hall 1994

Teaching:

Lectures per week	2 x 2 hours
Classwork sessions per week	2 x 2 hours
Module duration	5 weeks
Total contact hours	40
Private study and group working	60

Assessment information 2011 / 2012: