Authors: Rohit Kamleshwar Singh, Sangram Kakade, Dr. Nagsen Bansod, Dr. R. S. Deshpande
Abstract: Mathematics is the backbone of machine learning, providing the theoretical framework required to develop algorithms, optimize models, and interpret data patterns. The integration of mathematical disciplines such as linear algebra, probability theory, statistics, calculus, and optimization enables the construction of robust machine learning systems. This paper justify the essential mathematical concepts that underpin machine learning, covering major topics such as matrix operations, statistical inference, statistical inference, optimization techniques, and differential equations. Additionally, it impart how these mathematical tools contribute to several machine learning paradigms, such as deep learning, reinforce ment learning, supervised learning, and unsupervised learning. By understanding the function of mathematics in machine learning, researchers and practitioners can enhance model performance and develop innovative AI-driven solutions.
