Introduction to Artificial Intelligence

Intelligent Agents and Search Algorithms is your gateway to understanding how machines make decisions, solve problems, and act rationally in complex environments. In this course, you’ll explore the foundational principles that power intelligent systems—starting with agent architectures and environment types, and progressing into the design of goal-driven, rational behavior. You’ll examine how search enables AI systems to navigate uncertainty and make optimal choices, diving into uninformed and informed strategies such as breadth-first search, depth-first search, A*, and adversarial search. You’ll also learn how heuristics shape efficiency and performance—an essential concept for building scalable, high-performing AI systems. More than a theoretical overview, this course emphasizes applied skill-building. Through hands-on programming assignments and algorithm analysis, you’ll compare performance trade-offs, implement search strategies, and evaluate real-world problem-solving approaches. As part of CU Boulder’s MS in Artificial Intelligence, this course equips you with the conceptual clarity and technical foundation required for AI development, systems design, and advanced study. Whether you’re preparing to build intelligent systems or elevate your role in an AI-driven organization, mastering agents and search is a critical step forward in your career.

This course introduces the foundational principles of artificial intelligence through the lens of reasoning and decision-making under uncertainty. Students begin by examining how intelligent agents act in uncertain environments using probability theory, Bayes’ Rule, and independence assumptions to update beliefs—concepts that underpin probabilistic machine learning and data-driven decision-making. The course then explores Bayesian Networks as a structured framework for representing complex dependencies and performing inference, connecting to modern graphical models and causal reasoning. Building on this, students study probabilistic reasoning over time using temporal models such as Hidden Markov Models, with links to contemporary sequence modeling and state estimation in applications like speech recognition and robotics. Finally, the course addresses sequential decision-making through Markov Decision Processes, where students learn to compute optimal policies using value iteration, policy iteration, and the Bellman equation—ideas that form the foundation of modern reinforcement learning methods used in systems such as autonomous agents and game-playing AI.