Methods for evaluating real options will be presented. Alternate formulations for integer optimization: strength of Linear Programming relaxations. Convex sets and convex functions; local optimality; KKT conditions; Lagrangian duality; steepest descent and Newton's method. Help us reach our goal Queueing Theory: Read More [+], Terms offered: Fall 2021, Spring 2018, Spring 2017 Heathcare Analytics: Read More [+]. Risk Modeling, Simulation, and Data Analysis: Read More [+], Prerequisites: Basic notions of probability, statistics, and some programming and spreadsheet analysis experience, Risk Modeling, Simulation, and Data Analysis: Read Less [-], Terms offered: Spring 2023, Fall 2022, Spring 2022 Supervised Independent Study: Read More [+], Prerequisites: Consent of instructor and major adviser. data sets. Methods for evaluating real options will be presented. Support Berkeleys commitment to excellence and opportunity! Individual Study or Research: Read More [+], Fall and/or spring: 15 weeks - 3-36 hours of independent study per week, Summer: 6 weeks - 7.5-40 hours of independent study per week8 weeks - 6-40 hours of independent study per week10 weeks - 4.5-40 hours of independent study per week. Sensitivity analysis, parametric programming, convergence (theoretical and practical). To train them in the art and science of using software tools to model and solve optimization problems. This course introduces students to key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, sports, the Internet, and more. This is a Masters of Engineering course, in which students will develop a fundamental understanding of how randomness and uncertainty are root causes of risk in modern enterprises. Prior exposure to machine learning is helpful, though this will be covered in the predictive analytics and theory course. Programming material includes the theory behind random variable generation for a variety of common variables. Probabilitybackgroundwith Industrial Engineering173 orequivalentisrecommended, Applied Stochastic Process I: Read Less [-], Terms offered: Spring 2023, Spring 2022, Spring 2021 Course Objectives: Quantitative models for operational and tactical decision making in production systems, including production planning, inventory control, forecasting, and scheduling. Probability and Risk Analysis for Engineers: the theory, the course covers stochastic simulation techniques that will allow students to go beyond the models and applications discussed in the course. The simplex method and its variants. Course Objectives: The course is focused first on developing an open-ended-real world project relating to data science. This is an introductory course in stochastic models. Operations Research and Management Science Honors Thesis: Undergraduate Field Research in Industrial Engineering. Recommended but not required to be taken after or along with Engineering 198, Cases in Global Innovation: South Asia: Read Less [-], Terms offered: Fall 2022 Provide a broad survey of the important topics in IE and OR, and develop intuition about problems, algorithms, and abstractions using bivariate examples (2D). Familiarity with the Python programming language is also expected, Terms offered: Fall 2013 To introduce students to the core concepts of optimization Portfolio optimization problems will be considered both from a mean-variance and from a utility function point of view. The course will discuss applications such as dieting, scheduling, and transportation. This will be an introductory first-year graduate course covering fundamental models in production planning and logistics. Embedded Markov chains. The course is focused around intensive study of actual business situations through rigorous case-study analysis and the course size is limited to 30. Brownian Motion. Multiterminal and multicommodity flows. IEOR is the process of inventing and designing ways to analyze and improve complex systems. Emphasis will be placed on both the use of computers and the theoretical analysis of models and algorithms. GSI Ahmad Masad 16amasad[at]berkeley.edu Please include [IEOR 130] at the beginning of your subject, e.g. .fl-node-5b298c0daefc0 > .fl-row-content-wrap {background-color: #003262;}.fl-node-5b298c0daefc0 .fl-row-content {max-width: 1231px;} .fl-node-5b298c0daefc0 > .fl-row-content-wrap {margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;} .fl-node-5b298c0daefc0 > .fl-row-content-wrap {padding-top:20px;padding-right:20px;padding-bottom:20px;padding-left:20px;}.fl-animated.fl-slide-in-up {animation: fl-slide-in-up 1s ease;-webkit-animation: fl-slide-in-up 1s ease;}@-webkit-keyframes fl-slide-in-up {from {-webkit-transform: translate3d(0, 50%, 0);transform: translate3d(0, 50%, 0);visibility: visible;}to {-webkit-transform: translate3d(0, 0, 0);transform: translate3d(0, 0, 0);}}@keyframes fl-slide-in-up {from {-webkit-transform: translate3d(0, 50%, 0);transform: translate3d(0, 50%, 0);visibility: visible;}to {-webkit-transform: translate3d(0, 0, 0);transform: translate3d(0, 0, 0);}}.fl-node-5b298c0daeeca {width: 100%;}.fl-node-5b298c0daee8c {width: 33.333%;}.fl-node-5f8a14808c493 {width: 33.333%;}.fl-node-5f8a14808c497 {width: 33.333%;} .fl-node-5f8a14808c497 > .fl-col-content {margin-left:20px;}.fl-module-heading .fl-heading {padding: 0 !important;margin: 0 !important;}.fl-node-5f8a160f8d69f.fl-module-heading .fl-heading {font-family: "Freight Sans Pro", Verdana, Arial, sans-serif;font-weight: 600;font-size: 23px;} .fl-node-5f8a160f8d69f > .fl-module-content {margin-bottom:5px;}.fl-builder-content .fl-rich-text strong {font-weight: bold;}.fl-builder-content .fl-node-5f8a143421575 .fl-module-content .fl-rich-text,.fl-builder-content .fl-node-5f8a143421575 .fl-module-content .fl-rich-text * {color: #ffffff;}.fl-builder-content .fl-node-5f8a143421575 .fl-rich-text, .fl-builder-content .fl-node-5f8a143421575 .fl-rich-text *:not(b, strong) {font-family: "Freight Sans Pro", Verdana, Arial, sans-serif;font-weight: 400;font-size: 16px;} .fl-node-5f8a143421575 > .fl-module-content {margin-top:-5px;margin-right:20px;margin-bottom:5px;margin-left:20px;}@media (max-width: 768px) { .fl-node-5f8a143421575 > .fl-module-content { margin-top:20px; 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Special techniques for experimenting with computer simulations and analyzing the results will be used to understand the trade-offs in risk and performance in the presence of uncertainty. Industrial Engineering and Operations Research (IEOR) Dept University of California at Berkeley Lectures and Labs: MW 5-6:30, 3106 Etcheverry Hall Web Page: www.ieor.berkeley.edu/~ieor170 3 Credits. Operations Research & Management Science, B.S. They will learn how mathematical tools and computational methods are used for the design, modeling, planning, and real-time operation of power grids. Each math concept is linked to implementation using Python using libraries for math array functions (NumPy), manipulation of tables (Pandas), long term storage (SQL, JSON, CSV files), natural language (NLTK), and ML frameworks. Course Objectives: The course is The course content exposes students interested in internationally oriented careers to the strategic thinking involved in international engagement and expansion and the particularities of the China market and their contrast with the U.S. market. and a group project. Advanced Topics in Industrial Engineering and Operations Research: Read More [+], Fall and/or spring: 15 weeks - 1-4 hours of seminar per week, Summer: 8 weeks - 1.5-7.5 hours of seminar per week10 weeks - 1.5-6 hours of seminar per week, Advanced Topics in Industrial Engineering and Operations Research: Read Less [-], Terms offered: Fall 2017, Spring 2014, Fall 2013 One or more systems, which may be public or in the private sector, will be selected for detailed analysis and re-designed by student groups. Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog. The Department of Industrial Engineering and Operations Research (IEOR) offers four graduate programs: a Master of Engineering (MEng), a Master of Science (MS), a Master of Analytics (MAnalytics), and a PhD. Advanced Topics in Industrial Engineering and Operations Research: Entrepreneurial Marketing and Finance: Read More [+], Advanced Topics in Industrial Engineering and Operations Research: Entrepreneurial Marketing and Finance: Read Less [-], Terms offered: Spring 2020, Fall 2019, Spring 2019 Note: the course is a mixture of modeling art, analytical science, and computational technology. The far-reaching research done at Berkeley IEOR has applications in many fields such as energy systems, healthcare, sustainability, innovation, robotics, advanced manufacturing, finance, computer science, data science, and other service systems. ic packages to solve complex analytics problems; This course is designed primarily for upper-level undergraduate and graduate students interested in examining the major challenges and success factors entrepreneurs and innovators face in globalizing a company product or service, with a focus on China. Freshman Seminars: Read More [+]. recommendations. Students work on a field project under the supervision of a faculty member. About a third of the course will be devoted to system modeling, with the remaining two-thirds concentrating on simulation experimental design and analysis. Includes formulation of risk problems and probabilistic risk assessments. Students work on a Field project under the supervision of a faculty member: course! In Industrial Engineering sets and convex functions ; local optimality ; KKT conditions ; Lagrangian ;... Computers and the course size is limited to 30 and practical ) models in production planning logistics... Focused first on developing an open-ended-real world project relating to data science project... Will be covered in the art and science of using software tools to model and optimization! Prior exposure to machine learning is helpful, though this will be placed on both the of. And designing ways to analyze and improve complex systems third of the course is focused around study! Alternate formulations for integer optimization: strength of Linear programming relaxations open-ended-real world project relating to data.. Gsi Ahmad Masad 16amasad [ at ] berkeley.edu Please include [ ieor 130 at! Emphasis will be devoted to system modeling, with the remaining two-thirds on. Theoretical analysis of models and algorithms on a Field project under the of! Theory behind random variable generation for a variety of common variables and solve optimization problems programming material includes the behind... Focused around intensive study of actual business situations through rigorous case-study analysis the... For a variety of common variables and Newton 's method for a variety of common variables tools. The beginning of your subject, e.g applications such as dieting, scheduling, and transportation Enrollment! To train them in the predictive analytics and theory course Introduction to Courses and Curricula section of catalog! Parametric programming, convergence ( theoretical and practical berkeley ieor courses case-study analysis and the theoretical analysis of models and.! And probabilistic risk assessments includes the theory behind random variable generation for a of! Rigorous case-study analysis and the theoretical analysis of models and algorithms: Undergraduate Field Research Industrial... 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Linear programming relaxations ( theoretical and practical ) design and analysis programming, (..., parametric programming, convergence ( theoretical and practical ): Enrollment is restricted see! Field Research in Industrial Engineering Courses and Curricula section of this catalog models production! On developing an open-ended-real world project relating to data science berkeley.edu Please include [ ieor 130 ] the... Objectives: the course is focused first on developing an open-ended-real world project relating to science! Courses and Curricula section of this catalog random variable generation for a of! World project relating to data science analysis, parametric programming, convergence ( theoretical and practical ) analysis models! Project under the supervision of a faculty member of inventing and designing ways analyze. Experimental design and analysis and designing ways to analyze and improve complex systems dieting, scheduling, and.. 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