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) 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, business, and healthcare. The goal of this course is to provide students with the required knowledge to apply solution techniques in real-world situations.
ENGS 27 or ENGS 93 and either ENGS 20 or COSC 10; or instructor permission