This course is about the theory and practice of Artificial Intelligence. We will study modern techniques for computers to represent task-relevant information and make intelligent (i.e., satisficing or optimal) decisions towards the achievement of goals. The introduced methods are applicable throughout a large range of industrial, civil, medical, financial, robotic, and information systems. We will ask questions about AI systems such as: how to represent knowledge, how to effectively generate appropriate sequences of actions, how to deal with uncertainty in the world, how to learn from experience, and how to process and provide reward and punishment? We expect that by the end of the course students will have a thorough understanding of the algorithmic foundations of AI, how probability and AI are closely interrelated, and a strong appreciation of the big-picture aspects of developing fully autonomous intelligent agents. Near the end of the course we will spend several lectures learning about and discussing some important current application areas of AI. Prerequisite:15-212.