This course is about the formal models of knowledge and belief used in logic, philosophy, computer science, artificial intelligence, game theory, linguistics, and statistics. It is focused on four foundational questions:

• How should an agent's uncertainty be represented, and how are the different representations related?
• What, if anything, justifies representing an agent's uncertainty by a probability measure?
• How should an agent change her beliefs in response to new evidence?
• What is the relationship between an agent's graded beliefs, full beliefs, and the reasons that support them?

The final part of the course (time permitting) focuses on issues that arise in decision theory and in the move from a single agent to a group of agents.