Bolón-Canedo, VerónicaMorán-Fernández, LauraCancela, BraisAlonso-Betanzos, Amparo2026-04-132026-04-132023V. Bolón-Canedo, L. Morán-Fernádez, B. Cancela and A. Alonso-Betanzos, "Green Machine Learning", ESANN 2023 Proceedings - 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2023, p.269-278, https://doi.org/10.14428/esann/2023.ES2023-3978-2-87587-088-9https://hdl.handle.net/2183/47948Traballo presentado en: ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 04 - 06, October, 2023[Abstract]: Green machine learning refers to research that is more environmentally friendly and inclusive, not only by producing novel results without increasing the computational cost, but also by ensuring that any researcher with a laptop has the opportunity to perform high-quality research without the need to use expensive cloud servers. Efficient machine learning approaches (especially deep learning) are starting to receive some attention in the research community. This tutorial is concerned with the development of machine learning algorithms that optimize efficiency rather than only accuracy. We provide an overview of this recent field, together with a review of the novel contributions to the ESANN 2023 special session on Green Machine Learning.engAll rights reserved. This is the published version of the paper, distributed in accordance with ESANN's self-archiving policy, which allows authors to archive their work in any repository provided that full reference is made to the ESANN publication.Green machine learningEfficient deep learningComputational efficiencyconference outputopen access10.14428/esann/2023.ES2023-3