Applied Machine Learning Iii

This Special Issue focuses on applications of Machine Learning (ML) models in a wide range of fields and problems. It covers a wide range of learning algorithms, from classic ones such as linear regression, k-nearest neighbors, or decision trees, to newly developed algorithms such as deep learning and boosted tree models. It also discusses the challenges of dealing with big, missing, distorted, and uncertain data, as well as the importance of interpretability in ML models. Manuscripts are expected to report substantive results on a wide range of learning methods, discussing conceptualization of a problem, data representation, feature engineering, ML models, critical comparisons with existing techniques, and the interpretation of results.