Foundations of Intelligent Agents and Stochastic Decision-Making

In a world driven by data, artificial intelligence must constantly make choices under conditions of uncertainty. Understanding how intelligent agents evaluate options and make optimal decisions using probability is fundamental to modern computer science. This course guides you through the core mathematical and conceptual frameworks that power autonomous decision-making. You will transition from basic probability concepts to understanding how agents navigate complex, unpredictable environments. What you'll learn: - Understand the fundamental terminology of intelligent agents, utility theory, and environments. - Analyze simple and complex decision-making processes under uncertainty using probability. - Model stochastic processes using Markov chains and Markov Decision Processes. - Explore modern reinforcement learning concepts and how agents learn from environmental feedback. - Apply decision-making algorithms to practical scenarios using clean, modern Python code snippets. - Evaluate agent performance and decision boundaries in dynamic simulation environments. You will begin by mastering essential terminology and foundational probability concepts before moving on to agent architectures and stochastic modeling. Through step-by-step written explanations and structured code walk-throughs, you will see how mathematical theory translates into executable decision logic. This course is designed for aspiring software developers, data enthusiasts, and beginners curious about artificial intelligence, requiring no prior background in advanced mathematics. Start building your understanding of intelligent systems today.