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Course Offerings

Course Evaluation Results

Course Details

Fall 2018-2019
ORF 411 / ELE 411   Graded A-F, P/D/F, Audit

Sequential Decision Analytics and Modeling

Warren B. Powell

Students will develop mathematical modeling skills in the context of sequential decisions under uncertainty. Students will learn the five elements of a sequential decision problem: state variables, identifying and modeling decisions, uncertainty quantification, creating transition functions, and designing objective junctions. They will learn how to design policies, and the principles of policy search and evaluation in both offline and online settings. All concepts will be taught through a series of carefully chosen problems designed to bring out specific modeling features.

Sample reading list:
Warren Powell, Stochastic Optimization and Learning, (book in progress)
Warren Powell, Optimal Learning (selected Chapters)

Reading/Writing assignments:
There will be a problem set structured around each modeling segment, consisting of a mathematical modeling exercise and a programming assignment to implement the model and test policies.

Requirements/Grading:
Mid Term Exam - 20%
Take Home Final Exam - 30%
Problem set(s) - 50%

Other Requirements:
Statistical, design or other software use required
Open to Juniors, Seniors, and Graduate Students Only.

Prerequisites and Restrictions:
MAT 202, an introductory probability and statistics course (e.g. ORF 245 or ORF 309) and basic programming skills (primarily Matlab and Python)..

Other information:
While not a hard prerequisite, students will benefit if they have taken courses such as ORF 311, ORF 360, ORF 407 or ORF 418. The course may be of interest to students from any field that involves sequential decisions, but the material will require a facility with mathematical modeling.

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
22140 L01 11:00:00 am - 12:20:00 pm M W   Sherrerd Hall   101   Enrolled:32 Limit:42