Lectures and appropriate assignments on fundamental or applied topics of current interest in industrial engineering and operations research.

Course Description The purpose of this course is to provide graduate students with the fundamental understanding of the basic theory, models and algorithms for stochastic optimization, and with illustrations of how the theory and methods can be applied to many problems in contemporary machine learning and related disciplines. In the process, students will also learn about optimization under uncertainty and related analytical tools. Mathematical rigor is emphasized throughout the course. Students are expected to be interested in such rigor and willing to learn and practice it. Prerequisites Students should have completed a foundational course in optimization such as IEOR 240/262A or its equiv- alent. While not mandatory, students are strongly encouraged to concurrently enroll in IEOR 262B if they have not already completed it.

Repeat Rules