2019-2020 2020-2021 2021-2022 2022-2023 2023-2024
Browse
by subject...
    Schedule
view...
 

1 - 10 of 12 results for: OIT

OIT 245: Optimization and Simulation Modeling

This course provides basic skills in quantitative modeling. The objective is to familiarize students with the main steps in an analytical approach to business decision making: constructing an abstract model for a relevant business problem, formulating it in a spreadsheet environment such as Microsoft Excel, and using the tools of optimization, Monte Carlo simulation and sensitivity analysis to generate and interpret recommendations. The class will be taught in a lab style, with short in-class exercises done in small teams, focusing on a variety of applications drawn from online advertising, healthcare, finance, supply chain management, revenue and yield optimization.
Terms: Aut | Units: 3

OIT 247: Optimization and Simulation Modeling - Accelerated

The course is aimed at students who already have a background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth, than in OIT 245.
Terms: Aut | Units: 3
Instructors: Xu, K. (PI)

OIT 248: Optimization And Simulation Modeling - Advanced

This course is an advanced option in the menu of classes satisfying the Core requirement in Optimization and Simulation Modeling (OSM). It is an advanced version of OIT 245 and OIT 247 and it covers a similar (but slightly expanded) set of concepts pertaining to prescriptive analytics, including static optimization, Monte-Carlo simulation, decision trees and dynamic optimization, and reinforcement learning. The main differences are in the pace and depth, with OIT 248 covering each topic significantly faster and at a deeper level. Additionally, OIT 248 leverages Python instead of Excel for implementation and devotes more time to discussing practical issues that arise in real-world, data-driven implementations. By the end of the course, students should develop an in-depth mental framework of the topics and leave with a good understanding of how they fit within modern machine-learning / AI pipelines that aid decision-making in complex problems. The class is taught in an interactive st more »
This course is an advanced option in the menu of classes satisfying the Core requirement in Optimization and Simulation Modeling (OSM). It is an advanced version of OIT 245 and OIT 247 and it covers a similar (but slightly expanded) set of concepts pertaining to prescriptive analytics, including static optimization, Monte-Carlo simulation, decision trees and dynamic optimization, and reinforcement learning. The main differences are in the pace and depth, with OIT 248 covering each topic significantly faster and at a deeper level. Additionally, OIT 248 leverages Python instead of Excel for implementation and devotes more time to discussing practical issues that arise in real-world, data-driven implementations. By the end of the course, students should develop an in-depth mental framework of the topics and leave with a good understanding of how they fit within modern machine-learning / AI pipelines that aid decision-making in complex problems. The class is taught in an interactive style, focusing on a variety of applications drawn from advertising, healthcare, finance, supply chain management, revenue management and pricing, scheduling, and risk management. We emphasize that OIT 248 uses Python to teach analytics, but is not a course on Python or coding per se. Some prior coding experience is helpful but is not a strict requirement for the course.
Terms: Aut | Units: 3
Instructors: Iancu, D. (PI)

OIT 249: MSx: Data and Decisions

Data and Decisions teaches you how to use data and quantitative reasoning to make sound decisions in complex and uncertain environments. The course draws on probability, statistics, and decision theory. Probabilities provide a foundation for understanding uncertainties, such as the risks faced by investors, insurers, and capacity planners. We will discuss the mechanics of probability (manipulating some probabilities to get others) and how to use probabilities to make decisions about uncertain events. Statistics allows managers to use small amounts of information to answer big questions. For example, statistics can help predict whether a new product will succeed or what revenue will be next quarter. The third topic, decision analysis, uses probability and statistics to plan actions, such as whether to test a new drug, buy an option, or explore for oil. In addition to improving your quantitative reasoning skills, this class seeks to prepare you for later classes that draw on this material, including finance, economics, marketing, and operations. At the end we will discuss how this material relates to machine learning and artificial intelligence.
Terms: Aut | Units: 3
Instructors: Somaini, P. (PI)

OIT 606: Advanced Topics in Optimization

Exact topics TBD, but will include real-time optimization in different settings.
Terms: Aut | Units: 3
Instructors: Saban, D. (PI)

OIT 644: Research in Operations, Information and Technology

This year-long course takes a hands-on approach to learning about conducting research in Operations, Information and Technology. It will cover a broad spectrum of cutting-edge research in OIT from conceiving an idea to formulating a research problem, deriving results, and publication. The topical content will be customized to the specific interests of the enrolled students, but generally will be concerned with questions of operational interest.
Terms: Aut, Win, Spr | Units: 1 | Repeatable 15 times (up to 15 units total)

OIT 655: Foundations of Supply Chain Management

Driven by technology, data insights, and collaborations, supply chains have evolved from traditional cost centers to vital sources of competitive advantage for leading global companies. Yet, as recent events like pandemics, wars or severe (climate-change induced) weather events serve to remind us, such advancements have also led to heightened complexities and management challenges. Correspondingly, supply chain research has transitioned during the past 60+ years from addressing primarily operational questions related to production, inventory, or logistics to examining strategic issues on information sharing or incentive alignment among the many stakeholders involved in today¿s global supply chains, and to understanding the role of regulation or technology in improving designs and processes. Reflecting these trends, this course sets two main learning objectives. First, to survey some of the foundational tools and techniques used to model and understand supply chains, leveraging ideas fr more »
Driven by technology, data insights, and collaborations, supply chains have evolved from traditional cost centers to vital sources of competitive advantage for leading global companies. Yet, as recent events like pandemics, wars or severe (climate-change induced) weather events serve to remind us, such advancements have also led to heightened complexities and management challenges. Correspondingly, supply chain research has transitioned during the past 60+ years from addressing primarily operational questions related to production, inventory, or logistics to examining strategic issues on information sharing or incentive alignment among the many stakeholders involved in today¿s global supply chains, and to understanding the role of regulation or technology in improving designs and processes. Reflecting these trends, this course sets two main learning objectives. First, to survey some of the foundational tools and techniques used to model and understand supply chains, leveraging ideas from operations research, decision sciences, economics, and computer science. Second, to identify knowledge gaps and research opportunities by covering emerging topics such as supply chain financing, designing and operating socially responsible and environmentally sustainable supply chains, or using technology (AI, online platforms, distributed ledgers, remote sensing) to improve designs and processes. The precise selection of topics varies by year, depending on instructor and student interest. The course is structured as a combination of formal lectures covering some of the foundational topics and seminar-style discussions involving student presentations.
Terms: Aut | Units: 3
Instructors: Iancu, D. (PI)

OIT 691: PhD Directed Reading (ACCT 691, FINANCE 691, GSBGEN 691, HRMGT 691, MGTECON 691, MKTG 691, OB 691, POLECON 691, STRAMGT 691)

This course is offered for students requiring specialized training in an area not covered by existing courses. To register, a student must obtain permission from the faculty member who is willing to supervise the reading.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

OIT 692: PhD Dissertation Research (ACCT 692, FINANCE 692, GSBGEN 692, HRMGT 692, MGTECON 692, MKTG 692, OB 692, POLECON 692, STRAMGT 692)

This course is elected as soon as a student is ready to begin research for the dissertation, usually shortly after admission to candidacy. To register, a student must obtain permission from the faculty member who is willing to supervise the research.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

OIT 698: Doctoral Practicum in Teaching

Doctoral Practicum in Teaching
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable 25 times (up to 50 units total)
Filter Results:
term offered
updating results...
teaching presence
updating results...
number of units
updating results...
time offered
updating results...
days
updating results...
UG Requirements (GERs)
updating results...
component
updating results...
career
updating results...
© Stanford University | Terms of Use | Copyright Complaints