Discrete event simulation methodology emphasizing the statistical basis for simulation modeling and analysis. Overview of computer languages and simulation design applied to various industrial situations.

Explore a wide range of modern systems engineering principles and development methodologies. Address requirements engineering, systems definition, design and analysis, implementation, operation, and technical management.

An introduction to basic stochastic processes such as Poisson and Markov processes and their applications in areas such as inventory, reliability, and queueing.

Rigorous introduction to the theory of Beysian Statistical Inference. Bayesian estimation and testing. Conjugate priors. Noninformative priors. Bayesian computation. Bayesian networks and Bayesian signal processing. Various engineering applications.

Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.

Basic forecasting methods, ARIMA models, transfer functions.

This course introduces the available methods in machine learning, which methods are better in what situations, objective functions and errors, and how to tune parameters.