International Journal of Science, Engineering and TechnologyAuthors: K. Usha Pavani
Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have emerged as revolutionary technologies that are reshaping industries such as healthcare, finance, education, transportation, and cyber security. At the core of these technologies lies mathematics, which provides the theoretical and computational foundation necessary for machines to learn, analyze data, and make intelligent decisions. Mathematical concepts such as linear algebra, calculus, probability, statistics, and optimization are essential for designing and improving AI and ML algorithms. Linear algebra enables the representation and manipulation of large datasets through vectors, matrices, and tensors, which are fundamental components of neural networks and deep learning systems. Calculus plays a crucial role in optimizing machine learning models by computing derivatives and gradients that help minimize error functions during training. Probability and statistics are widely used for handling uncertainty, making predictions, analyzing patterns, and evaluating the reliability of models. These mathematical foundations allow AI systems to perform tasks such as image recognition, speech processing, natural language understanding, recommendation systems, and autonomous decision-making. Therefore, mathematics serves as the backbone of Artificial Intelligence and Machine Learning, enabling continuous advancements and intelligent automation across various domains.
DOI: https://doi.org/10.5281/zenodo.20548834
