This computer science course helps you to develop a conceptual understanding of the technologies behind artificial intelligence (AI) and the role of stochastic methods in guiding decision-making. We first discuss simple and complex decision-making and introduce different types of agents. We identify intelligent agents, simple reflex agents, simple reflex agents with internal state and goal-based and utility-based agents.

The course explains how to combine utility theory with probability to enable an agent to select actions that maximize its expected performance. We cover utility and maximum expected utility (MEU) thoroughly and before examining the theorem for utility function and value of information and information gathering by agents. This computer science course explores decision theory as a combination of probability, utility and decision systems and networks. We outline basic ideas and provide examples of reinforcement learning to help you understand AI.

We then show you how to use knowledge about the world to make decisions even when the outcomes of an action are uncertain. The course takes you through the Markov decision process (MDP), Partially Observable MDP, Dynamic decision networks (DDN) and game theory to help you master the science of probability. Our final section focuses on stochastic methods used to simulate random variables. The course lays out the basics of set theory, probability distribution and the Bayesian rule for conditional probability. This computer science course combines the study of probability and technology to help you understand the role of AI in improving analysis and decision-making in various fields.