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.

Topics Include

• Bayesian networks
• Influence diagrams
• Dynamic programming
• Reinforcement learning
• Partially observable Markov decision processes

Note: students who take the course for 4 units should expect to spend around 30 additional hours on the final project.

What You Need to Succeed

• A conferred bachelor’s degree with an undergraduate GPA of 3.5 or better
• Basic probability and fluency in a high-level programming language

What You Need To Get Started

Before enrolling in your first graduate course, you must complete an online application.

Don’t wait! While you can only enroll in courses during open enrollment periods, you can complete your online application at any time.

Once you have enrolled in a course, your application will be sent to the department for approval. You will receive an email notifying you of the department's decision after the enrollment period closes. You can also check your application status in your mystanfordconnection account at any time.

Learn more about the graduate application process.

How Much It Will Cost

See more about tuition and fees.