Decision Making under Uncertainty introduces the foundational ideas of making good decisions despite an unknown environment. This course will start with a review of probability and will mainly focus on solution techniques for single-stage and sequential decision problems. Specifically, the course will be divided into four main parts: (1) overview and probabilistic models; (2) solution techniques for single-stage decision problems; (3) model-based solution techniques for sequential decision problems; and (4) model-free solution techniques for sequential decision problems. The approaches for solving decision-making problems covered in this course are relevant for a wide range of fields including engineering, computer science, finance, supply chain management, transportation, and healthcare. The goal of this course is to provide students with the required knowledge to apply solution techniques in real-world situations.
ENGS 103 or permission of the instructor. Additionally, students should be proficient in a programming language such as Julia, Python, R, or MATLAB.
This course was previously offered as ENGG 177.