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@article{189888,
        author = {Dr. K. Shalini},
        title = {The Role of Mathematics in Artificial Intelligence and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {2644-2649},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189888},
        abstract = {Artificial Intelligence (AI) is the capacity of the machines to perform cognitive functions such as thinking, seeing, learning, solving problems, and making decisions. Its inspiration was the manner in which humans deploy their minds to observe, acquire knowledge, think and determine what to undertake. It already affects the homes of people, their places of work and political system. Very soon, we will have a robot which can drive cars to work, run warehouses as well as assist in looking after old people and small children. It brings such challenges as a non-understandable black box algorithm, unethical use of data, and even the disappearance of jobs, yet it can also help to solve some of the greatest problems of the contemporary society. The variety of stakeholders should work in collaboration to maximize accountability, transparency, privacy, and impartiality to create trust due to the high rate of growth of machine learning (ML), as the technology can independently acquire and evolve in all spheres of everyday life, and because the rapid pace of development poses a risk to increase the scope and the magnitude of the application of artificial intelligence. Mathematics is the foundation on which artificial intelligence (AI) and machine learning (ML) are built to give us the basic frameworks and tools to construct complex algorithms and models. The importance of specific areas of mathematics, including calculus, probability theory, linear algebra and optimization in the design of AI and ML systems. we discuss how to utilize calculus to solve the problems of training neural networks through gradient-based optimization algorithms and we discuss the importance of integration and differentiation in the process of backpropagation and loss optimization. Probability theory and its applications to Bayesian networks, Markov decision processes, and probabilistic graphical models, in particular, how it is used to deal with uncertainty and make predictions. Moreover, we discuss convex and non-convex optimization approaches to finding optimal solutions to machine learning, support vector machine (SVM) and deep learning systems.},
        keywords = {Artificial Intelligence, warehouses, algorithm, stakeholders, Robots, Deep Learning system.},
        month = {January},
        }