Applied Machine Learning Ⅱ
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 46236
Special Issue Editor
Interests: machine learning; evolutionary computation; artificial intelligence; pattern recognition; data mining and applications in forecasting, classification, regression, and optimization problems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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 an 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 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.
Assoc. Prof. Grzegorz Dudek
Guest Editor
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