Covers modeling of discrete-event dynamic systems and introduces methods for using these models to solve engineering design and analysis problems.

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.

An overview of the foundations and algorithms for solving nonlinear continuous optimization problems with special emphasis on the applications to machine learning and artificial intelligence.

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.

Study of concepts for the modeling and analysis of transportation networks, including systems perspective, static and dynamic networks, and emerging technologies and methods.

Topics include neural networks, support vector machines, classification trees, boosting and discriminant analyses. Intended for Ph.D. students and those seeking the M.S. in Statistics.

This class describes the available knowledge regarding statistical computing. Topics include random deviates generation, importance sampling, Monte Carlo Markov chain (MCMC), EM algorithms, bootstrapping, model selection criteria, (e.g. C-p, AIC, etc.) splines, wavelets, and Fourier transform.