ECE3093 Optimisation estimation and numerical methods

When you finish this unit, you should be able to:

1. 1
   On completing this unit, you will have learned advanced mathematical techniques for working efficiently and reliably with both deterministic and stochastic systems, and their use in solving problems frequently arising in engineering applications such as solving linear systems, solving systems of differential equations, handling noise, modelling control systems, time series analysis, and studying stability in dynamical systems. You will develop a rich set of techniques: Eigen analysis greatly simplifies the calculations for many numerical tasks; singular value decomposition and principal component analysis provide powerful tools for data compression and noise filtering; curve fitting methods for estimation, and optimisation tools add to the toolkit of techniques you will learn to enable you to tackle a range of practical engineering problems. You will also have learnt how to work with discrete and continuous random variables and some important distributions, random vectors and their covariance matrices, calculating best linear predictors, modelling using random sequences and stochastic processes in continuous time, autocovariance functions, transfer functions, spectral density and linear filters, ARMA models and finding best linear predictors for stationary processes.