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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18710547 |
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Table of Contents:
- <p><em><span>Artificial Intelligence (AI) and Machine Learning (ML) have rapidly transformed various sectors including healthcare, banking, transportation, education, and governance. AI refers to the ability of machines to simulate human cognitive functions such as learning, reasoning, and decision-making, while ML enables systems to learn from data and improve over time without explicit programming. Despite their technological appearance, these systems are fundamentally built upon mathematical principles. Mathematics provides the theoretical foundation and computational tools necessary for constructing intelligent algorithms and predictive models.</span></em></p> <p><em><span><span> </span>This paper highlights the essential role of mathematics in AI and ML, focusing on key domains such as linear algebra, calculus, probability theory, and optimization. Linear algebra enables efficient data representation through vectors and matrices, while calculus supports learning processes through gradient-based optimization and back propagation. Probability theory addresses uncertainty and supports predictive modeling, and optimization techniques help determine optimal model parameters. The paper emphasizes that a strong mathematical foundation is necessary to develop reliable, efficient, and transparent AI systems. As AI continues to expand across society, collaborative efforts are required to ensure accountability, fairness, and ethical deployment.</span></em></p>