This course gives theoretical and practical knowledge of methods to develop mathematical models from experimental data. Parametric and non-parametric methods; Review of modeling principles; Process identification from step response; Frequency response identification; Commonly used Signals, spectral Properties, persistent excitation; Correlation methods; Least squares identification; Recursive LS techniques; determining model orders; model validation; AR, MA modeling of system, linear prediction; Multidimensional systems; Application and case studies. Prerequisite Graduate Standing (Not be taken for credit with EE551)