Applications of Vedic Mathematics for Machine Learning

Abstract

In this paper, we explore the integration of principles from Vedic mathematics, an ancient system of mathematical techniques originating from Indian scriptures, into modern machine learning methodologies. While Vedic mathematics primarily focuses on arithmetic computations and problem-solving, its underlying principles of pattern recognition, optimization, and rapid mental calculations offer valuable insights for enhancing various aspects of machine learning algorithms. We discuss how techniques such as sutras (aphorisms) for problem-solving, rapid mental calculations, and pattern recognition can be adapted and applied in the context of machine learning. Furthermore, we explore potential applications of Vedic mathematics principles in algorithm design, optimization techniques, feature engineering, error analysis, and education within the machine learning domain. Through this integration, we aim to inspire novel approaches and improvements in machine learning algorithms and methodologies.