COR Electives

These are courses that count towards the 30 credit requirement.

The following courses count towards the 30 credits required for the MS degree. Other graduate courses may count towards the 30 credits required for the degree but must be approved by  [[rrkinc, Rex Kincaid]].

Math 552. Mathematical Statistics. Fall and Spring [3] Prerequisite(s): Math 551 or the equivalent. The mathematical theory of statistical inference. Possible topics include: maximum likelihood, least squares, linear models, methods for estimation and hypothesis testing.

COR 608. Statistical Decision Theory. Fall [3]. Pre-requisite: Equivalent of Math 351. Development and use of systematic procedures for assisting decision makers in evaluating alternative choices. Emphasis is on problem formulation, uncertainty and risk assessment, Bayes, minimax and other decision rules and applications. Problems will be solved using appropriate software tools.

COR 616. Design of Experiments. Fall [3]. Pre-requisite: Equivalent of Math 451. Topics covered in this course include the theory, design, conduct, and statistical analysis of experiments. Special emphasis is placed upon factorials and fractional factorials to optimize experimental resources.

COR 618. Models and Applications in Operations Research. Fall [3]. Pre-requisite: Equivalent of Math 323. A study of realistic and diverse Operations Research problems with emphasis upon model formulation, interpretation of results, and implementation of solutions. Topics include applications of linear programming, goal programming, decomposition of large-scale problems, and job scheduling algorithms. Problems will be solved using appropriate software packages.

COR 626. Linear Regression. Fall [3]. Pre-requisite: Equivalent of Math 451. Topics covered in this course include the theory, mechanics, and application of simple and multiple linear regression.

COR 638. Nonlinear Programming [3]. Pre-requisite: CSci 628 and the equivalent of Math 212. Topics include unconstrained optimization, nonlinear least squares, feasible-point methods, and penalty and barrier methods, with an emphasis on effective computational techniques.

COR 646. Business Analytics [3]. Business analytics (BA) refer to the use of data by organizations, including businesses, non-profits, and government entities, to extract insights and enhance decision-making processes. It finds application in various areas such as operations, marketing, finance, and strategic planning, among others. This course underscores that BA is not a theoretical field; its methodologies only hold significance when they can translate into practical insights that enhance the speed, dependability, and quality of decisions.

COR 648. Integer Programming [3]. Pre-requisite: CSci 628. This course emphasizes coding of Mixed Integer Programming (MIP) models, interpreting solver results, and deepening the understanding of solving techniques. Solver techniques include Lagrangian relaxation, Benders decomposition, column generation, as well as branch and bound, polyhedral theory and cutting planes.

COR 656. Reliability [3]. Pre-requisites: Equivalent of Math 451 and CSci 141. Introduction to probabilistic models and statistical methods used in analysis of reliability problems. Topics include models for the lifetime of a system of components and statistical analysis of survival times data. Problems will be solved using appropriate software tools.

COR 658. Discrete Optimization [3]. Pre-requisite: CSci 628 and the equivalent of CSci 303. Topics include relaxation techniques, constructive heuristics, improving search techniques (simplex method, simulated annealing, and tabu search), branch and bound schemes, and valid inequalities for branch and cut methods. Problems will be solved using appropriate software tools.

COR 668. Supply Chain Optimization [3]. This course provides a deeper understanding of the optimization principles that underpin modern machine learning and data science. Topics include regularized regression, support vector machines, and reinforcement learning models. Students will explore real-world applications and gain hands-on experience, formulate and solve data-driven optimization problems efficiently.

COR 676. Statistical Analysis of Simulation Models [3]. Pre-requisites: Equivalent of Math 351, Math 401 and CSci 141. This courses introduces statistical techniques used in the analysis of simulation models. The first half of the course develops techniques for determining appropriate inputs to a simulation model, and the last half develops analysis techniques that are applied to the output of a simulation model.

COR 678. Network Location Theory [3]. Network location problems arise in many diverse applications. Examples include locating facilities, sensors, components, vehicles, people, services, and actuators. The course will include topics from classical location theory (covering, center and median problems) as well as more recent topics in the literature.

COR 688. Topics. Fall or Spring (1, 2, or 3 credits, depending on material) Staff. Pre-requisite: Will be published in he preregistration schedule. May be repeated for different topics. A treatment of Master's level topics of interest not routinely covered by existing courses. Material may be chosen from various areas of computational operations research. Topics classes that have been offered in the past include convex optimization, statistical computing, regression, design of experiments, facility location theory, computational probability, bootstrapping, scale-free networks, stochastic optimization, and knowledge discovery.

COR 699. Research Project in Computational Research [2]. Fall and Spring. Graded P (Pass) or F (Failure). Pre-requisite: Permission of Graduate Director. Students will select a faculty advisor and committee in their area of specialization within computational operations research, prepare a research proposal abstract for approval by the department's director of graduate studies, undertake a research project, and write a paper describing their research. This course is normally taken after a student has completed 18 credit hours toward the M.S. degree in Computational Operations Research. 

CSci 516. Introduction to Machine Learning. Fall [3]. Pre-requisites: Algorithm, Linear Algebra. Machine learning (ML) is the study of predictive models whose performance can be improved by incorporating additional data or experience. This course will give an overview of the theory and practice of machine learning, focusing primarily on deterministic ML methods for classification and regression. Topics include decision trees, linear and nonlinear regression, artificial neural networks, support vector machines and kernel methods, ensemble methods, clustering methods, dimension reduction techniques, mixture models, and naive Bayes methods. We will also look at practical concerns such as performance evaluation, data preprocessing, and hyperparameter tuning. Cross-listed with CSCI-416

CSci 520. Introduction to Neural Networks. Fall [3]. Pre-requisites: Instructor permission required. The is a project-based course aimed at providing a fundamental understanding of Artificial Neural Networks (ANN). Topics include the underlying principles of ANNs; backpropagation for computing gradients for training ANNs; elements of training such as stochastic gradient descent, dropout, and initialization; and common types of ANNs, such as convolutional networks, recurrent networks, and generative-adversarial networks.

CSci 526. Simulation. Fall [3]. Pre-requisites: Calculus, Data Structures. Introduction to simulation. Discrete and continuous stochastic models, random number generation, elementary statistics, simulation of queuing and inventory systems, Monte Carlo simulation, point and interval parameter estimation.

CSci 616. Stochastic Models in Computer Science. Fall [3]. Pre-requisites: Knowledge of Discrete Mathematics and Calculus. An introduction to stochastic models, problem solving, and expected value analysis as applied to algorithms and systems in computer science. Topics include probability, discrete and continuous random variables, discrete-time Markov chains, and continuous-time birth-death processes.

CSci 653. Analysis of Algorithms. Fall [3]. Pre-requisite: CSci 503 or CSci 539. Algorithm design techniques including divide-and-conquer, dynamic programming and greedy method. Analysis methods including worst case and average case. Additional topics chosen from among amortized analysis, lower bound theory and NP-completeness.

CSci 626. Data Analysis and Simulation. Fall or Spring [3] Staff. Pre-requisite: Knowledge of probability and statistics. Methods of discrete event simulation. Markov chains. Simulation of open and closed networks of queues. Simulation of non-stationary Poisson processes. Transient and steady-state analysis. Event list algorithms and data structures. Theoretical and empirical tests of randomness.

CSci 680. Statistical Computing. Fall [3]. Pre-requisites: Probability, Statistics. Topics include linear regression, linear least squares, matrix factorization, nonlinear regression, Gauss-Newton methods, maximum likelihood estimation, parameter estimation, quasi-Newton methods, Monte Carlo integration, and bootstrap methods.

Pubp 616. Time Series Econometrics. Fall or Spring [3] Staff. Pre-requisite: Pubp 603. This course is an introduction to the econometric analysis of time series data. Topics include ARIMA models, forecasting, analysis of nonstationary series, unit root tests, co-integration and principles of modeling.