Contemporary AI needs intelligent agents that efficiently learn to make good and reliable decisions. Design of such agents that behave appropriately in various circumstances are highly demanded (AI, ML, data mining, financial modelling, natural language processing, bioinformatics, web search and information retrieval, algorithm design, system design, network analysis, and more). Such intelligent agents need to reason with uncertain information and limited computational resources. Effective decision making requires the knowledge about: • the agent’s environment and its dynamics (including the presence of other intelligent agents), • the agent’s goals and preferences • the agent’s abilities to observe and influence the environment. This course introduces sequential decision-making and computational methods supporting decision-making. The course helps to develop the mathematical reasoning skills crucial for areas inherently involving uncertainty. These skills can serve as the foundation for further study in any application area you choose to pursue.