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This course introduces the fundamental concepts of Complex Systems and Artificial Intelligence, with emphasis on modeling, adaptation, emergence, self-organization, and collective intelligence. Students explore complex adaptive systems, Genetic Algorithms, and Swarm Intelligence techniques such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO).
The course combines theoretical concepts with practical applications in optimization and AI. Students learn how evolutionary and swarm-based methods can be used to solve complex problems, analyze convergence, perform feature selection, and improve machine-learning model performance. Practical sessions include maze pathfinding using Genetic Algorithms and feature-selection tasks using swarm-based optimization methods.
The course aims to develop both conceptual understanding and practical skills for applying nature-inspired computational methods to real-world optimization and artificial intelligence problems.