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May be repeated.

Individual investigations of any phase of Industrial Engineering. May be repeated in separate terms. Prerequisite: Consent of instructor.

Nature of probabilistic models for observed data; discrete and continuous distribution function models; inferences on universe parameters based on sample values; control charts, acceptance sampling, and measurement theory. Credit is not given towards graduation for both IE 300 and CEE 202; credit is also not given towards graduation for both IE 300 and BIOE 310. Prerequisite: MATH 241.

Linear Optimization - Simplex method, duality, and sensitivity analysis, Transportation and Assignment Problems, Network Optimization Models, Dynamic Programming, Nonlinear optimization, and Discrete optimization. Credit is not given for both IE 310 and CEE 201. Prerequisite: Credit or concurrent registration in MATH 257 or MATH 415.

Facility planning, plant layout design, and materials handling analysis; determination of facilities requirements, site selection, materials flow, use of analytical and computerized techniques including simulation, and applications to areas such as manufacturing, warehousing, and office planning. Prerequisite: Credit or concurrent enrollment in IE 310.

Individual investigations or studies of any phase of Industrial Engineering. May be repeated in separate terms. Prerequisite: Consent of instructor.

Introduces students to algorithm design, computer programming in C++, and database SQL queries. Provides the fundamental methods, concepts and principles of these topics to give students enough breadth to use these techniques in their jobs and to prepare them to pursue advanced topics in these areas. There will be weekly programming assignments to implement algorithms and SQL covered in the class. 3 undergraduate hours. 4 graduate hours. Prerequisite: CS 101 or CS 124 or equivalent.

Modeling and analysis of stochastic processes. Transient and steady-state behavior of continuous-time Markov chains; renewal processes; models of queuing systems (birth-and-death models, embedded-Markov-chain models, queuing networks); reliability models; inventory models. Familiarity with discrete-time Markov chains, Poisson processes, and birth-and-death processes is assumed. Same as CS 481. 3 undergraduate hours. 4 graduate hours. Prerequisite: IE 310.

Practical methods of optimization of large-scale linear systems including extreme point algorithms, duality theory, parametric linear programming, generalized upper bounding technique, price-directive and resource-directive decomposition techniques, Lagrangian duality, Karmarkar's algorithm, applications in engineering systems, and use of state-of-the-art computer codes. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: IE 310 and MATH 257 or MATH 415.

Operations research techniques applied to problems in manufacturing and distribution. Single and multi-stage lot sizing problems, scheduling and sequencing problems, and performance evaluation of manufacturing systems. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: IE 310.

Teaches students both the core concepts and underlying mechanics of, step by step, message by message, bit for bit, exactly how trillions of dollars in notional value are automatically traded daily around the globe, whether it is stocks, bonds, options, futures, currencies, crypto, etc. High Frequency Trading will provide students with an exciting introduction both to the modern world of automated finance and to many exciting technologies that power it. Where does the "actual" real-time price of a particular asset come from at any point in time? How exactly is it being calculated and by who or what? Is there even a single price or are there multiple, and are any of those prices actually correct? Just how fast can modern traders process market data or execute trades and how do they accomplish this? 4 undergraduate hours. 4 graduate hours. Credit is not given toward graduation for: Credit is not given if student received credit in IE 498/IE 598 Electronic Trading or IE 498/IE 598 High Frequency Trading. Prerequisite: Should have an understanding of programming and data structures and be proficient in coding in at least one programming language (typically python, C/C++, java, javascript, etc). Students who have taken CS 225 would have the requisite knowledge, but it is not required students have taken this course.

In Work and Organization Design, we will learn about models and theories of work and organization design. By the end of the class you will be able to distinguish between good and bad jobs, including important characteristics of organizations, based on empiric evidence. This class will also provide tools and skills to analyze and redesign jobs to make bad jobs into good ones. We will discuss various implementation strategies to intelligently apply redesigns, which improves their success. 3 undergraduate hours. 4 graduate hours. Prerequisite: IE 340 or consent of instructor.

Same as EPSY 456 and PSYC 456. See EPSY 456.

Independent study of advanced problems related to industrial engineering. 1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated. Prerequisite: Consent of instructor.

Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. 1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated in the same or separate terms if topics vary to a maximum of 9 hours.

Machine Learning includes the design and the study of algorithms that can learn from experience, improve their performance and make predictions. This course is designed specifically and exclusively for MSFE first semester students. It features rigorous coding exercises in Python and acts as preparation for later courses. Students will learn the concepts behind different supervised machine learning algorithms and implement them in Python using advanced packages; pandas, NumPy, and scikit-learn. All the data for this course features unique real-world financial datasets.

An introduction to queueing systems and their applications in engineering. Topics include both classical single-stage models and queueing networks. Students will learn how to apply key ideas and methods of queueing theory, such as: Markov processes, embedded Markov chains, PASTA property, reversibility, productform stationary distributions, stochastic stability, asymptotic analysis. Prerequisite: IE 410 or an equivalent graduate stochastic processes course.

Finite dimensional convex optimization problems; characterization of optimal solutions; iterative algorithms for differentiable and nondifferentiable problems; distributed optimization algorithms; robust problems and solutions; applications of convex optimization models. Prerequisite: ECE 490 or IE 411; MATH 416; MATH 444.

Statistical tools that are fundamental for financial modeling, analyzing financial data and further studies in financial engineering. Topics include summary statistics, statistical plots, point estimation, accuracy and precision, confidence interval, Monte Carlo simulation, maximum likelihood estimation, normal mixture, resampling, hypothesis testing, simple linear regression, multiple linear regression, variable selection, regression diagnostics, autocorrelation, moving average models, filtering, autoregressive models, ARIMA, forecasting, and selected additional topics. Implementations are done using R. Credit is not given toward graduation for: Credit is not given for both IE 522 and SE 524. Prerequisite: IE 300 and MATH 461.

Review of C++ programming: structures, classes, I/O, C++ standard libraries, Recursion. Linear Algebra tools and MILP solvers in C++. Computational aspects of probability, statistics and simulation in C++. Methods: Root-finding, Taylor's Expansion, FFTs, Dynamic Programming, Monte Carlo Methods. Financial computing case studies in C++. Prerequisite: CS 225.

Basic optimization models, theory and methods for financial engineering including linear, quadratic, nonlinear, dynamic integer, and stochastic programming; applications to portfolio selection, index fund tracking, asset management, arbitrage detection, option pricing and risk management; optimization software for classes of optimization problems. Projects requiring building optimization models based on financial market data and solutions using optimization solvers. May be repeated in the same or separate semesters if topics vary to a maximum of 4 hours. Prerequisite: FIN 500 and MATH 257 or equivalent.

Preparing MSFE students for successful careers in financial engineering. Topics include financial engineering career paths, MSFE concentrations and electives, preparing resumes and cover letters, interview procedures and preparation, networking, and job offer and salary negotiation. Lectures will be supplemented with seminars given by MSFE alumni, MSFE practicum project sponsors, and practitioners. They will give insights on financial markets, employment opportunities, market trends, internship and full-time job searching, and much more. Approved for S/U grading only. May be repeated in separate terms. Prerequisite: Restricted to MS: Financial Engineering Students only.

Application of mathematical, statistical, computing, machine learning and data analytics tools to a real life financial engineering team project sponsored by the industry. Projects in each semester are different and may require different skill sets. Regular meetings with the sponsor required. May be repeated in separate terms for a total of 8 hours. Prerequisite: IE 522, FIN 500, and MSFE program approval. Restricted to MS: Financial Engineering Students only.

Presentation and discussion of significant developments in industrial engineering. Approved for S/U grading only. May be repeated.

Independent study of advanced problems related to industrial engineering. May be repeated in the same or separate terms if topics vary to a maximum of 12 hours. Prerequisite: Consent of instructor.

Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Approved for Letter and S/U grading. May be repeated in the same or separate terms if topics vary.