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Explore fundamental artificial intelligence concepts through this comprehensive university course covering essential AI algorithms, techniques, and applications. Begin with foundational topics including intelligent agents, environments, and problem-solving approaches before diving into search algorithms and heuristic methods like A*. Master game theory principles and adversarial search techniques including MiniMax and AlphaBeta algorithms, then examine optimization strategies and hash functions for efficient data structures. Investigate evolutionary computing methods and gain insights into AI ethics considerations in modern applications. Delve into reinforcement learning fundamentals, starting with bandit algorithms and progressing through Markov Decision Processes, dynamic programming, and Monte Carlo methods. Learn temporal difference learning techniques and conclude with an introduction to neural networks and deep learning architectures. Practice implementing these concepts through hands-on assignments that reinforce theoretical understanding with practical programming experience.
