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Explore the fundamentals of applying machine learning techniques to cosmological research in this comprehensive lecture from the V Joint ICTP-Trieste/ICTP-SAIFR School on Cosmology. Learn how modern computational methods and artificial intelligence are revolutionizing our understanding of the universe's structure, evolution, and fundamental properties. Discover the theoretical foundations and practical applications of machine learning algorithms in analyzing large-scale cosmological datasets, including galaxy surveys, cosmic microwave background data, and N-body simulations. Examine how neural networks, deep learning, and statistical inference methods can extract meaningful cosmological parameters and test theoretical models against observational data. Understand the challenges and opportunities presented by big data in cosmology, including data preprocessing, feature extraction, and model validation techniques. Gain insights into current research frontiers where machine learning is being used to study dark matter, dark energy, galaxy formation, and cosmic structure formation. This foundational class provides essential knowledge for researchers and students interested in the intersection of artificial intelligence and cosmological science, preparing you for advanced applications of computational methods in understanding the cosmos.
