Dear Colleagues,

Machine learning (ML) is one of the most exciting fields of computing today. In recent decades, ML has become an entrenched part of everyday life and has been successfully used to solve practical problems. The application area of machine learning is very broad, including engineering, industry, business, finance, medicine, and many other domains. ML covers a wide range of learning algorithms, including classic ones such as linear regression, k-nearest neighbors, or decision trees, through support vector machines and neural networks, to newly developed algorithms such as deep learning and boosted tree models. In practice, it is quite challenging to properly determine the appropriate architecture and parameters of ML models so that the resulting learner model can achieve sound performance for both learning and generalization. Practical applications of ML bring additional challenges such as dealing with big, missing, distorted, and uncertain data. In addition, interpretability is a paramount quality that ML methods should aim to achieve if they are to be applied in practice. Interpretability allows us to understand ML model operation and raises confidence in its results.

This Special Issue focuses on applications of ML models in a diverse range of fields and problems. Application papers 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. Specific attention will be given to recently developed ML methods, such as deep learning and boosted tree models.

Dr. Grzegorz Dudek
Guest Editor

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