Author(s): Aklesh Kumar

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming diverse fields, from healthcare to finance, and their foundation lies deeply rooted in mathematics. Core mathematical disciplines such as linear algebra, calculus, probability, statistics, and optimization provide the structural framework that enables machines to learn from data and make intelligent predictions. Linear algebra plays a vital role in representing and manipulating large datasets, powering neural networks, and handling multidimensional computations. Calculus enables optimization processes by calculating gradients and updating parameters to minimize loss functions in deep learning models. Probability and statistics serve as the backbone for modeling uncertainty, developing predictive algorithms, and assessing data-driven inferences. Optimization theory ensures efficient learning by finding the most suitable solutions within high-dimensional spaces, which is critical for training complex models. Furthermore, advanced topics like information theory and numerical analysis contribute to improving efficiency and accuracy in algorithm design. This mathematical synergy transforms abstract data into actionable insights, allowing systems to recognize patterns, classify information, and make autonomous decisions. Understanding these mathematical principles not only enhances algorithmic performance but also promotes interpretability and innovation in AI applications. Hence, the role of mathematics is indispensable, as it provides both the theoretical foundation and practical tools for the development and advancement of AI and ML technologies.

DOI: 10.33545/27076571.2024.v5.i2b.186

Pages: 168-172 | Views: 613 | Downloads: 467

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