This document provides an overview of uncertainty in artificial intelligence and probabilistic reasoning. It discusses sources of uncertainty like uncertain input, knowledge, and output. Probabilistic reasoning uses probability to represent uncertain knowledge. The document introduces basic probability notation including propositions, atomic events, unconditional probability, conditional probability, independence, and Bayes' rule. It explains how to perform inference using full joint probability distributions and marginalization. The document was prepared by Prof. Khushali B Kathiriya and provides an introduction to representing and reasoning with uncertainty in AI systems.