Foundations of Artificial Intelligence

Artificial intelligence is reshaping how we solve complex problems, yet its power rests on a foundation of classic algorithms and systematic logic. To build effective intelligent systems, you must first understand the mathematical and computational principles that guide machine decision-making. This comprehensive text-based course guides you from the absolute basics of state-space search to modern concepts in knowledge representation. You will transition from a beginner to a confident practitioner capable of analyzing and designing core AI problem-solving strategies. Through clear explanations and structured written exercises, you will learn to model real-world challenges as computational search problems. What you'll learn: - Understand foundational AI concepts, history, and the definition of rational agents - Master state-space search techniques including heuristic, greedy, and A* search algorithms - Solve complex constraint satisfaction problems using backtracking and constraint propagation - Apply propositional and first-order logic to formalize knowledge and reason systematically - Explore modern AI paradigms including basic probabilistic reasoning and decision-making under uncertainty - Analyze search optimization and game-playing algorithms like Minimax and Alpha-Beta pruning Starting with essential terminology and the historical context of intelligent agents, the course transitions into deep dives of search spaces, game theory, and logical reasoning frameworks. You will complete your journey by exploring how modern probabilistic methods handle real-world uncertainty. This course is designed for beginners, computer science students, and software developers looking for a rigorous, conceptual introduction to AI with no prior background in the subject required. Begin your journey into the mathematical and logical heart of artificial intelligence today.