Cases in Global Innovation: Read More [+], Fall and/or spring: 8 weeks - 2 hours of lecture per week, Cases in Global Innovation: Read Less [-], Terms offered: Prior to 2007 Supply chain analysis is the study of quantitative models that characterize various economic trade-offs in the supply chain. Introduce the different technologies used to develop simulation models and simulator products in order to become critical consumers of simulation study results. Terms offered: Spring 2014, Fall 2011, Fall 2009. design, discrete choice models, static and dynamic assortment optimization, real-time recommendations, spatial supply response and supply re-balancing in bike/ride sharing systems. To train them in the art and science of using software tools to model and solve optimization problems. The actual subjects covered may include: Convex analysis, duality theory, complementary pivot theory, fixed point theory, optimization by vector space methods, advanced topics in nonlinear algorithms, complexity of mathematical programming algorithms (including linear programming). Familiarize students in leading methodologies for solving integer optimization problems, and techniques in these methodologies. Cases will include both U.S. companies seeking to enter emerging markets and emerging market companies looking to expand within their own nations or into markets in developed nations. Computing technology has advanced to the point that commonly available tools can be used to solve practical decision problems and optimize real-world systems quickly and efficiently. It builds upon a basic course in probability theory and extends the concept of a single random variable into collections of random variables known as stochastic processes. This course prepares technical and business minded students for careers focused on professional and management track careers in high technology. The course will discuss applications such as dieting, scheduling, and transportation. business/industry challenges using Python packages such as Pandas, NumPy, Matplotlib, scikit- 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. Modeling with integer variables; branch-and-bound method; cutting planes. Prior exposure to machine learning is helpful, though this will be covered in the predictive analytics and theory course. Discussion, practice, and review of fundamentals, issues, and best practices in teaching for any engineering course. advanced analytics courses. Terms offered: Spring 2019, Fall 2015, Spring 2015, Supervised Independent Study and Research. Location MWF, 10:00-11:00am Online via Zoom. Polynomial time algorithms. data sets. Spring 2018: IEOR 262B - Mathematical Programming II. Sensitivity analysis, parametric programming, convergence (theoretical and practical). 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. Beginning with the Fall 2021 application cycle, rising sophomores will also be eligible to apply. If you just want to print information on specific tabs, you're better off downloading a PDF of the page, opening it, and then selecting the pages you really want to print. Course Objectives: This course provides an introduction to the field of Industrial Engineering and Operations Research through a series of lectures by IEOR faculty. Review of linear and nonlinear optimization models, including optimization problems with discrete decision variables. A project course for students interested in applications of operations research and engineering methods. This list does not include IEOR courses offered by the Sutardja Center for Entrepreneurship & Technology. Supervised group study and research by lower division students. Supervised Group Study and Research: Read More [+]. Course Objectives: Provide an introduction to the field of Industrial Engineering and Operations Research through a series of lectures. The course is focused around intensive study of actual business situations through rigorous case-study analysis. The material covered in the course includes internet auctions, procurement, service facility location, sevice quality management, capacity planning, airline ticket pricing, financial plan design, pricing of digital goods, call center management, service competition, revenue management in queueing systems, information intermediaries, and health care. Prerequisites: Students should have a solid knowledge of calculus, including multiple variable integration, such as MATH1A and MATH1B or MATH16A and MATH16B, as well as programming experience in Matlab or Python. Current Readings in Innovation: Read More [+], Prerequisites: Background: upper level standing or graduate student, any school, Fall and/or spring: 15 weeks - 3 hours of seminar per week, Current Readings in Innovation: Read Less [-], Terms offered: Spring 2011, Spring 2010, Spring 2009 understand the array of mathematical toolkits provided by the Python packages covered. .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 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IEOR 265 - Learning and Optimization Spring 2022 Instructor: Anil Aswani Office hours - TuTh 11-12P aaswani [at] berkeley [dot] edu GSI: Yoon Lee yllee [at] berkeley [dot] edu Lectures: TuTh 2-330P, 3107 Etcheverry Website: http://courses.ieor.berkeley.edu/ieor265 Optional Textbooks: The last part of the course will deal with inverse decision-making problems, which are problems where an agent's decisions are observed and used to infer properties about the agent. . This is an introductory course in stochastic models. Course Objectives: 1. Operations Research and Management Science Honors Thesis: Read Less [-], Terms offered: Prior to 2007 To complement the theory, the course also covers the basics of stochastic simulation. develop custom Python scripts and functions to perform analytic computations; understanding of supply chain management. exploratory analytics to systems analytics in an industry context, including communication of Prerequisites: This course is open to freshman and sophomore students from any department. 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