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Explore fundamental artificial intelligence concepts and techniques through this comprehensive graduate-level course from Memorial University's Computer Science department. Master algorithmic approaches and data structures essential for modern AI problem-solving environments, including search algorithms, game theory, evolutionary computing, reinforcement learning, and neural networks. Learn about intelligent agents and their environments, then progress through heuristic search methods and the A* algorithm, hash functions and hash tables for efficient data management. Dive into game theory fundamentals covering matrix games and Nash equilibrium, followed by advanced game-playing algorithms like MiniMax and AlphaBeta search with various enhancements. Discover evolutionary computing principles and their applications in optimization problems. Gain hands-on experience with reinforcement learning concepts including bandit algorithms, Markov Decision Processes, Monte Carlo methods, and temporal difference learning. Conclude with an introduction to neural networks and deep learning architectures. Throughout the course, apply learned techniques to simple games through practical assignments that reinforce theoretical concepts with real-world implementation experience.
