Industrial Engineering and Operations Research (IEOR) Dept University of California at Berkeley Lecture: MW 12-1, 3113 Etcheverry Hall, Lab: F 2-4, 1173 Etcheverry This course explores how databases are designed, implemented, used and maintained, with an emphasis on industrial and commercial Design and implementation of databases, with an emphasis on industrial and commercial applications. learn, Bokeh, and relevant optimization and simulation software. Economics of Supply Chains: Read More [+], Prerequisites: Basics Optimization and Probability (IndEng 240, IndEng 241, or equivalent), Economics of Supply Chains: Read Less [-], Terms offered: Spring 2023, Spring 2017, Spring 2015 With more than 4,000 alumni, 20 faculty, 20 advisory board members and 400 students, the IEOR department is a rapidly growing community equipped with tools and resources to make a large impact in industry, academia, and society. This course will cover topics related to the interplay between optimization and statistical learning. Group Studies, Seminars, or Group Research: Read Less [-], Terms offered: Summer 2023 Second 6 Week Session, Fall 2019, Fall 2016 The IEOR department plans to offer the following courses in the Spring 2022 semester. Faculty research in Berkeley IEOR specializes in stochastic processes, optimization, and supply chain management. Control and Optimization for Power Systems: Terms offered: Spring 2009, Spring 2007, Spring 2006. use machine learning to provide the adaptation. visualize analytic results in graphical form; Introduction to Production Planning and Logistics Models: Terms offered: Fall 2012, Spring 2005, Spring 2004, Terms offered: Spring 2021, Spring 2014, Spring 2013. competition, revenue management in queueing systems, information intermediaries, and health care. Includes formulation of risk problems and probabilistic risk assessments. The course is focused around intensive study of actual business situations through rigorous case-study analysis. Mathematical Programming I: Read More [+], Mathematical Programming I: Read Less [-], Terms offered: Spring 2023, Spring 2022, Spring 2021 Credit Restrictions: Students will receive no credit for Ind Eng 171 after taking UGBA105. Spring 2017: IEOR 268 - Applied Dynamic Programming. Bounds and approximations. IEOR leverages computing to better manage the massive amounts of information available today. and other social sciences, and engineering and in particular, data science research on analyzing large Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. 30% Notebook with Lecture Notes. Student Learning Outcomes: Learning goals include technical communication and project presentation. Course Objectives: This course provides an introduction to the field of Industrial Engineering and Operations Research through a series of lectures by IEOR faculty. The technical material will be presented in the context of engineering team system design and operations decisions. Branch and Bound; Cutting plane methods; polyhedral theory. Spring 2018: IEOR 268 - Applied Dynamic Programming. Terms offered: Spring 2022, Spring 2021, Spring 2020, Spring 2019, Spring 2018, Spring 2017. , control, finance, data mining, operations research. Prior exposure to optimization is helpful but not strictly necessary. Prerequisites: IEOR 240 Optimization Analytics, IEOR 241 Risk Modeling & Simulation Analytics, IEOR 242 Applications in Data Analysis. goldberg@ieor.berkeley.edu. One of the grand challenges of this century is the modernization of electrical power networks. Join the online learning revolution! It will start with basic programming topics using Python and cover Course does not satisfy unit or residence requirements for bachelor's degree. This undergraduate course will focus on fundamental models and algorithms for RM. Final exam not required. Prerequisites: INDENG165; INDENG173; INDENG172 or STAT134. Credit Restrictions: Students will receive no credit for INDENG174 after completing IND ENG 131. 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. The course will start with a quick review of 222: the basics of Brownian motion, martingales, Ito's calculus, risk-neutral pricing in continuous time models. Optimization Analytics: Read More [+], Prerequisites: Basic analysis and linear algebra, and basic computer skills and experience, Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week, Terms offered: Fall 2022, Fall 2021, Fall 2020 Design and analysis of models and algorithms for facility location, vehicle routing, and facility layout problems. Concentrations - UC Berkeley IEOR Department - Industrial Engineering & Operations Research Home / Academics / Master of Engineering / Concentrations Master of Engineering Apply Ranked #2 in the nation! Monte Carlo simulations are used in a weekly laboratory to model systems that may be too complex to approximate accurately with deterministic, stationary, or static models; and to measure the robustness of predictions and manage risks in decisions based on data-driven models. The simplex method; theorems of duality; complementary slackness. Introduction to Financial Engineering: Read More [+], Prerequisites: 162 or 262A, course in probability, or consent of instructor, Introduction to Financial Engineering: Read Less [-], Terms offered: Spring 2023, Fall 2022, Spring 2022 Work. develop custom Python scripts and functions to perform analytic computations; This course will cover topics related to healthcare analytics, including: optimizing chronic disease management, designing matching markets for health systems, developing predictive analytics models, and managing resource utilization. Introduction to Machine Learning and Data Analytics: Read More [+]. Theory of optimization for constrained and unconstrained problems. Individual Study for Doctoral Students: Read More [+], Individual Study for Doctoral Students: Read Less [-]. Logistics Network Design and Supply Chain Management: paths, project management and equipment replacement. understand relevant mathematical concepts that are used in systems that process data; Prerequisites: Prerequisites include the ability to write code in Python, and a probability or statistics course. Sample topics include, but are not limited to, resource allocation and pricing under uncertain sequential demand, mechanism design, discrete choice models, static and dynamic assortment optimization, real-time recommendations, spatial supply response and supply re-balancing in bike/ride sharing systems. Survey of solution techniques and problems that have formulations in terms of flows in networks. This course will study and draw connections between disparate fields to trace the development and influence of this view. Specialized strategies by integer programming solvers. Students work on a field project under the supervision of a faculty member. Applications to practical problems from engineering and data science. Selected topics in mathematical programming. The course is focused around intensive study of actual business situations through rigorous case-study analysis and the course size is limited to 30. understanding of supply chain management. Supply chain analysis is the study of quantitative models that characterize various economic trade-offs in the supply chain. Supply chain analysis is the study of quantitative models that characterize various economic trade-offs in the supply chain. IEOR is the process of inventing and designing ways to analyze and improve complex systems. We are committed to ensuring that all students have equal access to educational opportunities at UC Berkeley. Learn more. Since 1909, distinguished guests have visited UC Berkeley to speak on a wide range of topics, from philosophy to the sciences. Industrial Engineering and Operations Research Courses Search Courses. Production Systems Analysis: Read More [+], Prerequisites: INDENG160, INDENG173, INDENG162, INDENG165, and ENGIN120, Production Systems Analysis: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 The 190 series cannot be used to fulfill any engineering requirement (engineering units, courses, technical electives, or otherwise). Formerly Engineering 120. Advanced seminars in industrial engineering and operations research. A Bivariate Introduction to IE and OR: Read Less [-], Terms offered: Spring 2019, Fall 2015, Spring 2015 This year, Berkeley IEOR alum Sujit Chakravarthy, is making a $25,000 Big Match to support the IEOR Fund. Course Objectives: On the other hand, the Master of Analytics focuses on . Prerequisites: upper division standing. Operations Research & Management Science, B.S. This course will introduce graduate and upper division undergraduate students to modern methods for simulating discrete event models of complex stochastic systems. 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). 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. Prerequisites: Graduate Standing or ASE (Academic Student Employee) Status, Fall and/or spring: 15 weeks - 2 hours of seminar per week, Subject/Course Level: Industrial Engin and Oper Research/Professional course for teachers or prospective teachers, GSI Proseminar on Teaching Engineering: Read Less [-], Terms offered: Fall 2010, Fall 2008, Spring 2008 Endless discovery, industry engagement and exciting career opportunities. Probabilitybackgroundwith Industrial Engineering173 orequivalentisrecommended, Applied Stochastic Process I: Read Less [-], Terms offered: Spring 2023, Spring 2022, Spring 2021 Course Objectives: Applications on semiconductor manufacturing or other industrial settings. The use of mathematical optimization models as a framework for analyzing financial engineering problems will be shown. Course Objectives: 2. Instructors Type Term Exam Solution Flag (E) Flag (S) Munoz Some programming experience/literacy is expected, Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week, Introduction to Machine Learning and Data Analytics: Read Less [-], Terms offered: Fall 2022 Minimum cost flows. Study of algorithms for non-linear optimization with emphasis on design considerations and performance evaluation. We will focus primarily on both quantitative and qualitative issues which arise in the integrated design and management of the entire logistics network. Through these examples, exercises in R, and a comprehensive team project, students will gain experience understanding and applying techniques such as linear regression, logistic regression, classification and regression trees, random forests, boosting, text mining, data cleaning and manipulation, data visualization, network analysis, time series modeling, clustering, principal component analysis, regularization, and large-scale learning. Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Students will work on group projects along with Focus primarily on both quantitative and qualitative issues which arise in the of... Both quantitative and qualitative issues which arise in the supply chain the interplay between optimization and Learning. Other hand, the Master of Analytics focuses on optimization is helpful not. Risk assessments under the supervision of a faculty member, distinguished guests visited. 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