Small groups of students undertake an industry-sponsored project under faculty guidance.

Introduction to probability, probability distributions, point estimation, confidence intervals, hypothesis testing, linear regression, and analysis of variance. Crosslisted with MATH 3770 and CEE 3770. Also, credit not awarded for both ISYE 3770 and MATH 3670.

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.

Covers state-of-the-art Monte Carlo simulation techniques. These techniques will be used to model and solve a variety of real-world problems from several diverse areas in science and engineering, including supply chain analysis and design, pattern recognition, VLSI design, network reliability, financial engineering, and molecular biology.

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.