Course Detail

Online Optimization - Applications in Operations Mgmt 2 (36921)

Course Description by Faculty

  • Gupta, Varun Niazadeh, Rad
  • Content
    Sequential decision making, learning, and optimization are at the core of operations of online platforms and marketplaces. In this full course, which is the second (and more advanced) part of a two-part course, we (i) introduce analytical tools and techniques in online optimization, online algorithm design, online learning, and optimal stopping theory, (ii) learn how to design and analyze algorithms that make data-driven decisions on-the-fly, (iii) study how this algorithmic toolbox can be used for operations management and market design in various application domains such as sponsored search, video-ads, online retail, and operations of non-profit organizations, and finally (iv) learn how these connections help with designing better matching algorithms, ad allocations, pricing mechanisms, recommendation systems, nudging mechanism, etc. The topic of each lecture is based on one particular application domain, and we heavily rely on mathematical tools that were covered in part one (36920). 
  • Prerequisites
    BUSN 36920 (or instructor consent) required: strict.  PhD students only: strict.  Non-Booth students require instructor consent: strict.
    Restrictions
    • PhD - students only

    • Strict Prerequisite

  • Grades
    No pass/fail grades.  No auditors.
    Restrictions
    • No auditors

    • No pass/fail grades

  • No Syllabus Available
  • Spring 2022Section: 36921-50F 1:30PM-4:30PMHarper Center3SW - Seminar RoomIn-Person Only
Description and/or course criteria last updated: January 25 2022