Meta-learning. Application in AutoML and data science

Meta-learning is one of the fastest-growing areas of research in the field of machine learning (ML) and studies methods of obtaining effective models and solutions by adapting ML processes and intelligent data analysis. Adaptation typically involves using information from the experience of solving other tasks, and adaptive processes can utilize ML approaches. AutoML deals with automating machine learning processes and is a highly relevant topic directly related to meta-learning. Meta-learning and AutoML help artificial intelligence learn to choose the most suitable self-learning methods and find new solutions faster without user intervention. The publication is aimed at researchers in the field of machine learning, intelligent data analysis, and artificial intelligence, and can also be useful for students and graduate students.