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Machine Learning: Theoretical Foundations, Practical Applications, Ethical Implications, and Future Prospects - Exploring Challenges and Applications in Emerging Technologies
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Author(s): Abiodun Alagbada (Bauhaus University, Weimar, Germany), Temitope Comfort Iroko (University of Wisconsin--Milwaukee, USA)and Steve Tchoneteck (Rice University, USA)
Copyright: 2027
Pages: 24
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/407565
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Abstract
Machine Learning (ML) has rapidly emerged as a vital component of Artificial Intelligence (AI), transforming various sectors through predictive models, pattern recognition, and data-driven insights. This article examines the foundational theories underlying ML, key practical applications in high-impact industries, preserving ethical and societal considerations, and future trends. The authors explore how classical supervised and unsupervised learning methods have evolved to include sophisticated techniques such as deep learning, transfer learning, and reinforcement learning. Critically, they also evaluate the significant ethical challenges in algorithmic fairness, transparency, and data privacy alongside the growing concerns of ML's environmental impact and the digital divide. Finally, they discuss the trajectory of ML, touching on quantum computing, federated learning, Edge AI, and lifelong learning as promising avenues for future research that aim to create more robust, equitable, and sustainable systems
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