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Course Criteria
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3.00 Credits
Abbreviated version of ESD.72 with which it shares the lectures on reliability and probabilistic risk assessment (RPRA) and decision analysis (DA). Cost-benefit analysis is omitted.
Prerequisite:
Prereq: Calculus II (GIR)
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3.00 Credits
A survey of techniques for analyzing how the choice of materials, processes, and design determine properties, performance, and cost. Topics include production and cost functions, mathematical optimization, evaluation of single and multi-attribute utility, decision analysis, materials property charts, and performance indices. Students use analytical techniques to develop a plan for starting a new materials-related business.
Prerequisite:
Prereq: Permission of instructor
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3.00 Credits
Introduces the methodology and then develops applications to large-scale engineering systems, such as the design and construction of mega projects; the impacts of organization on system performance; and the interrelationships between technical systems and the social/political context in which such systems operate.
Prerequisite:
Prereq: Permission of instructor
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4.00 Credits
Introduction to mathematical modeling, optimization, and simulation, as applied to manufacturing. Specific methods include linear programming, network flow problems, integer and nonlinear programming, discrete-event simulation, heuristics and computer applications for manufacturing processes and systems. Restricted to Leaders for Global Operations students.
Prerequisite:
Prereq: Calculus II (GIR)
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4.00 Credits
Modeling and analysis of uncertainty and variation. Covers probability models and distributions, regression, and basic statistical procedures pertinent to manufacturing and operations. Introduces experimental and robust design, statistical process control, forecasting, and data-mining. Students use a data analysis package, such as JMP, Minitab, or MATLAB. Primarily for Leaders for Global Operations students.
Prerequisite:
Prereq: Calculus II (GIR)
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4.00 Credits
Advanced introduction to the theory and application of statistics and data-mining, concentrating on techniques used in management science, finance, consulting, engineering systems, and bioinformatics. First half builds the statistical foundation for the second half, with topics selected from sampling, including the bootstrap, theory of estimation, testing, nonparametric statistics, analysis of variance, categorical data analysis, regression analysis, MCMC, EM, Gibbs sampling, and Bayesian methods. Second half focuses on data-mining, supervised learning, and multivariate analysis. Topics selected from logistic regression; principal components and dimension reduction; discrimination and classification analysis, including trees (CART), partial least squares, nearest neighbor and regularized methods, support vector machines, boosting and bagging, clustering, independent component analysis, and nonparametric regression. Uses statistics software packages, such as R, S+, MATLAB, and SAS, for data analysis and data-mining.
Prerequisite:
Prereq: 6.431, 15.085, or 18.440; 18.06 or 18.700
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2.00 Credits
Provides an introduction to data mining (i.e., machine learning), a class of methods that that assist in recognizing patterns and making intelligent use of massive amounts of electronic data collected via the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, medical databases, search engines, and social networks. Topics selected from logistic regression, association rules, tree-structured classification and regression, cluster analysis, discriminant analysis, and neural network methods. Presents examples of successful applications in areas such as credit ratings, fraud detection, marketing, customer relationship management, and investments. Introduces data-mining software.
Prerequisite:
Prereq: 15.060 or 15.075
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4.00 Credits
Designed for students who have some acquaintance with probability and/or statistics and want exposure to a wider range of topics and examples. Begins with a brief review of statistics and regression by addressing advanced topics, such as variable selection, data and regression diagnostics, visualization, and Bayesian and robust methods. The remainder starts with data-mining, including stratified sampling, classification, logistic regression, and clustering. These topics are followed by time series analysis and forecasting, design of experiments and analysis of variance, and process control. Students use statistical computing systems, including Excel add-ins and stand-alone packages. Includes case studies involving finance, management science, consulting, and engineering systems.
Prerequisite:
Prereq: 6.431, 15.060, or permission of instructor
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3.00 Credits
Statistically based experimental design inclusive of forming hypotheses, planning and conducting experiments, analyzing data, and interpreting and communicating results. Topics include descriptive statistics, statistical inference, hypothesis testing, parametric and nonparametric statistical analyses, factorial ANOVA, randomized block designs, MANOVA, linear regression, repeated measures models, and application of statistical software packages. Alternate years.
Prerequisite:
Prereq: 6.041, 16.09, or permission of instructor
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2.00 Credits
Application-oriented introduction to systems optimization focusing on understanding system tradeoffs. Introduces modeling methodology (linear, integer and nonlinear programming) and simulation methods, with applications in production planning and scheduling, inventory planning and supply contracts, logistics network design, facility sizing and capacity expansion, yield management, electronic trading and finance.
Prerequisite:
Prereq: 1.145 or permission of instructor
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