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Explore evolutionary machine learning techniques applied to engineering design challenges through comprehensive lecture slides presented by Lukáš Sekanina from Brno University of Technology at the Johns Hopkins Summer Workshop on Language and Speech Processing (JSALT 2025). Discover how evolutionary algorithms can be integrated with machine learning approaches to solve complex engineering optimization problems, covering theoretical foundations, practical implementation strategies, and real-world applications across various engineering domains. Learn about the intersection of bio-inspired computational methods and modern ML techniques, examining case studies that demonstrate the effectiveness of evolutionary approaches in automated design processes, parameter optimization, and system configuration tasks within engineering contexts.
