Decision Making Under Uncertainty
This course is designed to increase awareness and appreciation for why uncertainty matters, particularly for aerospace applications. 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. Applications cover: air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration.
Please note: students who take the course for 4 units should expect to spend around 30 additional hours on the final project.
A conferred Bachelor’s degree with an undergraduate GPA of 3.5 or better.
Basic probability and fluency in a high-level programming language.
- Bayesian networks
- Influence diagrams
- Dynamic programming
- Reinforcement learning
- Partially observable Markov decision processes
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.
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