This Artificial Intelligence course is based on Stanford University’s CS221 curriculum, offering a structured introduction to the core principles that power intelligent systems. Designed with academic rigor yet accessible explanations, the course guides learners through the foundations of AI from theory to application.
Students begin with an overview of Artificial Intelligence and quickly progress into machine learning techniques such as linear classifiers, neural networks, and model generalization. The course then explores search strategies including dynamic programming, uniform cost search, and A* algorithms — essential tools for problem solving in AI systems.
A major focus is decision-making models, where learners study Markov Decision Processes and reinforcement learning to understand how intelligent agents evaluate actions and optimize outcomes. The curriculum also covers game-playing strategies like minimax and alpha-beta pruning, demonstrating how AI systems simulate reasoning and planning.
Each topic builds analytical thinking and algorithmic understanding, preparing learners to design intelligent solutions across robotics, automation, and data-driven applications. By the end, students gain a strong conceptual framework that bridges academic AI theory with practical problem solving — making this course ideal for serious beginners and aspiring AI
