Prerequisites: INDENG165; INDENG173; INDENG172 or STAT134. Credit Restrictions: Students will receive no credit for INDENG174 after completing IND ENG 131. 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 course content exposes students interested in internationally oriented careers to the strategic thinking involved in international engagement and expansion. Experimenting with Simulated Systems: Read More [+], Prerequisites: 165 or equivalent statistics course, and some computer programming background, Instructors: Ross, Schruben, Shanthikumar, Experimenting with Simulated Systems: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 Industrial Engineering and Operations Research142 Introduction to Machine Learning and Data Analytics Search Courses Exams Instructors Type Term Exam Solution Flag (E) Flag (S) Grigas Midterm 1 Fall 2019 Solution Flag Syllabi Instructors Term Download Flag Grigas Fall 2019 Download Flag Home| Contact Us This is an introductory course in stochastic models. IEOR informs business strategy and operations to help leaders of industry and government make better decisions that save time and resources. To train the students in the selection of appropriate techniques to be used for integer optimization problems. Embedded Markov chains. Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog. Design of such systems requires familiarity with human factors and ergonomics, including the physics and perception of color, sound, and touch, as well as familiarity with case studies and contemporary practices in interface design and usability testing. The course is intended for graduate students at the Masters level looking for a concrete introduction Familiarity with algorithm design and mathematical maturity recommended, Fundamentals of Revenue Management: Read Less [-], Terms offered: Fall 2020, Fall 2019, Fall 2018 the instructor in order to solidify the lectures into practical experience using Python for analytics. Companies can partner with IEOR to engage and recruit students. Seminar on selected topics from financial and technological risk theory, such as risk modeling, attitudes towards risk and utility theory, portfolio management, gambling and speculation, insurance and other risk-sharing arrangements, stochastic models of risk generation and run off, risk reserves, Bayesian forecasting and credibility approximations, influence diagrams, decision trees. All courses are subject to change. The course will put this into the larger context of the political, economic, and social climate in several South Asian countries and explore the constraints to doing business, as well as the policy changes that have allowed for a more conducive business environment. While these two programs share some core technical courses, the IEOR Master of Engineering program prepares students for engineering leadership and offers a curriculum with a balance of management and technical content. This course will not require pre-requisites and will present the core concepts in a self-contained manner that is accessible to Freshmen to provide the foundation for future coursework. doctoral students formulate their research designs. Supervised group study and research by lower division students. Portfolio optimization problems will be considered both from a mean-variance and from a utility function point of view. 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. Advanced topics in information management, focusing on design of relational databases, querying, and normalization. Final exam not required. Credit Restrictions: Students will receive 2 units for 120 after taking Civil Engineering 167. Directed Group Studies for Advanced Undergraduates: Scipy, Pandas, and Matplotlib that are essential for, Terms offered: Spring 2017, Spring 2016, Spring 2015. options. Conditional Expectation. Course Objectives: Provide an introduction to the field of Industrial Engineering and Operations Research through a series of lectures. Special Topics in Industrial Engineering and Operation Research: Read More [+], Prerequisites: Upper level standing or graduate student, Fall and/or spring: 15 weeks - 2-3 hours of lecture per week, Summer: 6 weeks - 5-7.5 hours of lecture per week10 weeks - 3-4.5 hours of lecture per week, Special Topics in Industrial Engineering and Operation Research: Read Less [-], Terms offered: Spring 2014, Fall 2008, Spring 2008 Convex sets and convex functions; local optimality; KKT conditions; Lagrangian duality; steepest descent and Newton's method. Algorithms for selected network flow problems. Design and analysis of models and algorithms for facility location, vehicle routing, and facility layout problems. Supervised Independent Study and Research: Terms offered: Fall 2022, Fall 2021, Fall 2020, Applied Data Science with Venture Applications, Terms offered: Spring 2023, Spring 2022, Fall 2021. , LTI systems, spectral analysis, and frameworks for learning from data. Major topics in the course include design of service processes, layout and location of service facilities, demand forecasting, demand management, employee scheduling, service quality management, and capacity planning. This course surveys topics related to the design of products and interfaces ranging from alarm clocks, cell phones, and dashboards to logos, presentations, and web sites. Research projects on current topics in information technology. advanced analytics courses. Students will understand the operation of power networks from a control and optimization perspective. We recently sat down with MoonSoo Choi to discuss his time as an undergraduate student and his current role as Senior Manager of Data Science at Walmart. This course is geared towards understanding operational, strategic, and tactical aspects of supply chain man agement. IEOR 130; IEOR 142; IEOR 150; IEOR 151; IEOR 153; IEOR 160; IEOR 161; IEOR 162 Grader: TBD. Programming material includes the theory behind random variable generation for a variety of common variables. Supervised Independent Study: Read More [+], Prerequisites: Consent of instructor and major adviser. Discussion, practice, and review of fundamentals, issues, and best practices in teaching for any engineering course. 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. Advanced seminars in industrial engineering and operations research. Help us reach our goal Applications in forecasting and quality control. The PDF will include all information unique to this page. Elementary queueing models; comparing single- and multiple-server queues. Group Studies, Seminars, or Group Research: Read Less [-], Terms offered: Summer 2023 Second 6 Week Session, Fall 2019, Fall 2016 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. They will also manage hypothetical portfolios throughout the course. Dynamic programming formulation of deterministic decision process problems, analytical and computational methods of solution, application to problems of equipment replacement, resource allocation, scheduling, search and routing. Specialized strategies by integer programming solvers. 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. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, Markov chains, LTI systems, spectral analysis, and frameworks for learning from data. The course introduces modern open source, computer programming tools, libraries, and code samples that can be used to implement data applications. Introductory graduate level course, focusing on applications of operations research techniques, e.g., probability, statistics, and optimization, to financial engineering. The first part of the course will cover statistical modeling procedures that can be defined as the minimizer of a suitable optimization problem. Share an intellectual experience with faculty and students by reading "Interior Chinatown" over the summer, attending author Charles Yu's live event on August 26, signing up for L&S 10: The On the Same Page Course, and participating in fall program activities. This course will cover topics related to the interplay between optimization and statistical learning. The course deals with discrete optimization problems and their complexity. Formulation and model building. Learn more about our game-changing alumni, and view our recent newsletters. Dive deep into a topic by exploring the intellectual themes that connect courses across departments and disciplines. Topics will vary from year to year. Theory of optimization for constrained and unconstrained problems. Supply Chain and Logistics Management: Read More [+], Supply Chain and Logistics Management: Read Less [-], Terms offered: Spring 2014, Fall 2011, Fall 2009 The course will put this into the larger context of the political, economic, and social climate in several South Asian countries and explore the constraints to doing business, as well as the policy changes that have allowed for a more conducive business environment. Credit Restrictions: Students will receive no credit for INDENG156 after completing INDENG256. Note: the course is a mixture of modeling art, analytical science, and computational technology. Convex Optimization and Approximation: Read More [+], Prerequisites: 227A or consent of instructor, Convex Optimization and Approximation: Read Less [-], Terms offered: Spring 2023 Algorithms for integer optimization problems. 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 Teach strengths and weaknesses of different approaches for a foundation for selecting methodologies. Recommended, but not required to be taken after or along with Engineering 198, Fall and/or spring: 15 weeks - 2 hours of lecture per week, Cases in Global Innovation: China: Read Less [-], Terms offered: Prior to 2007 Algorithms for integer optimization problems. Semi-Markov processes with emphasis on application. The main focus is on design and analysis of models and algorithms for matching, pricing, incentivizing, and personalizing on such platforms. Introduce students to modern techniques for developing computer simulations of stochastic discrete-event models and experimenting with such models to better design and operate dynamic systems. Alternate formulations for integer optimization: strength of Linear Programming relaxations. Students work on a field project under the supervision of a faculty member. Grading: The grading option will be decided by the instructor when the class is offered. Credit Restrictions: Course may be repeated for credit with consent of instructor. A course on financial concepts useful for engineers that will cover, among other topics, those of interest rates, present values, arbitrage, geometric Brownian motion, options pricing, & portfolio optimization. The field has made significant strides on both theoretical and practical fronts. be used to fulfill any engineering unit or elective requirements. To acquire skills in the best modeling approach that is suitable to the practical problem at hand. ic packages to solve complex analytics problems; IEOR 160: Nonlinear and Discrete Optimization Professor Javad Lavaei, UC Berkeley Instructor: Javad Lavaei Time: Fridays, 10am-12pm Location: 159 Mulford TAs: SangWoo Park (spark111 AT berkeley.edu) and Yatong Bai (yatong_bai AT berkeley.edu) Grader: Natalie Andersson (natalieandersson AT berkeley.edu) Credit Restrictions: Students will receive no credit for INDENG165 after completing STAT135. To train students in how to actually apply each method that is discussed in class, through a series of labs and programming exercises.5. Please use this as a guide for planning purposes. Innovations that we will discuss include collaborative forecasting, social media, online procurement, and technologies such as RFID. Course Objectives: This course provides an introduction to the field of Industrial Engineering and Operations Research through a series of lectures by IEOR faculty. Sujit's $25k Big Match will be unlocked once 50 gifts are made to the IEOR fund. designed to prepare students for the applied analytics problems and projects they will encounter in This seminar and discussion class aims to survey current and classic research on innovation and help Grading Based on: 30% Class Attendance and Participation ; 30% Notebook with Lecture Notes Analytics Lab: Read More [+]. Upon completion of the course students will have learned how to: Directed Group Studies for Advanced Undergraduates: Read More [+], Prerequisites: Senior standing in Engineering, Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week, Directed Group Studies for Advanced Undergraduates: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 Control and Optimization for Power Systems: Read More [+]. 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