AA 228 / CS 238 Decision Making under Uncertainty

This course introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Applications cover air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration.

basic probability and fluency in a high-level programming language.

• You will gain a broad fundamental understanding of the mathematical models and solution methods for decision making (exercises, three quizzes).
• You will be able to implement and extend key algorithms for learning and decision making (two programming projects).
• You will be able to identify an application of the theory in this course and formulate it mathematically (proposal).
• You will gain a deep understanding of an area of particular interest and apply it to a problem (final project).
• You will be able to critique approaches to solving decision problems (peer review).

This year we will be using the textbook titled Algorithms for Decision Making, MIT Press, 2022. Printed copies are available for purchase or you may download it for free as a PDF. Details are here.

The textbook serves as the official lecture notes.