Decision Making Under Uncertainty
Fee may apply
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.
Basic probability and fluency in a high-level programming language.
- Bayesian networks
- Influence diagrams
- Dynamic programming
- Reinforcement learning
- Partially observable Markov decision processes
Note on Course Availability
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 certificate homepage.
|Dates:||September 23 - December 6, 2019|
Provides Stanford University credit that may later be applied towards a graduate degree or certificate. Includes access to online course materials and videos for the duration of the academic quarter. Starting Autumn 2016 there is a $100 fee per course for courses dropped before the drop deadline. Click here for more information about our Registration Policies.
NotesEnrollment Dates: August 1 to September 9, 2019
This course may not currently be available to learners in some states and territories.