Keywords:-

Keywords: Linear Algebra, Mathematical Logic, Machine Learning, Data Science, Optimization, Reasoning, Problem Design

Article Content:-

Abstract

This paper introduces mathematically designed problems that illustrate how algebraic and logical reasoning underpin learning, transformation, and decision-making processes in AI systems. Mathematics forms the foundation of Machine Learning (ML) and Data Science (DS), with Linear Algebra and Mathematical Logic as two key pillars. Linear Algebra provides computational tools for data representation, model formulation, and optimization, while Mathematical Logic supports reasoning, inference, and explain ability. Together, they bridge numerical computation with logical reasoning, enabling the design of efficient and interpretable models. This paper highlights how integrating algebraic and logical principles strengthens both theoretical understanding and practical applications in ML and DS.

References:-

References

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Gore, H., Gargote, A., & Khairkhar, V. (2026). Problem Design in Linear Algebra and Mathematical Logic: A Foundation for Machine Learning and Data Science. International Journal Of Mathematics And Computer Research, 14(03), 66-70. https://doi.org/10.47191/ijmcr/v14iSPC3.14