Dear Colleagues,

Machine learning (ML) is one of the most exciting fields of computing today. Over recent decades, ML has become an entrenched part of everyday life and has been successfully used for solving 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 the classical 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 ML models and their applications in a diverse range of fields and problems. Papers are expected reporting 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 interpretation of results. Specific attention will be given to recently developed ML methods such as deep learning and boosted tree models.

Prof. Dr. Grzegorz Dudek
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• neural networks
• decision trees
• deep learning
• data mining
• natural language processing
• computer vision
• supervised learning
• unsupervised learning
• reinforcement learning
• evolutionary computation