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Explore fundamental artificial intelligence concepts and techniques through this comprehensive university-level course from Memorial University's Computer Science department. Master algorithmic approaches and data structures essential for modern AI problem-solving environments, with each theoretical concept reinforced through practical assignments involving simple games. Delve into core topics including intelligent agents and environments, problem-solving methodologies, and various search algorithms including the powerful A* heuristic search. Learn game theory fundamentals covering matrix games and Nash equilibrium, then advance to minimax algorithms with alpha-beta pruning for strategic decision-making. Discover evolutionary computing principles and genetic programming techniques for optimization problems. Gain hands-on experience with reinforcement learning concepts, starting with bandit algorithms and action value methods, progressing through Markov Decision Processes and dynamic programming. Explore Monte Carlo methods and temporal difference learning approaches that form the foundation of modern AI systems. Conclude with an introduction to neural networks and deep learning architectures that power today's most advanced AI applications. Throughout the course, apply learned techniques through practical assignments and participate in competitive programming challenges like the Connect 4 tournament to solidify understanding of AI algorithms in game-playing scenarios.
