Mathematics Model Used in Artificial Intelligence (AI) and Machine Learning (ML)

Rajdeep

Abstract: Mathematics is the cornerstone of Artificial Intelligence (AI) and Machine Learning (ML), providing the essential tools, frameworks, and methodologies required to develop advanced models and algorithms. In this paper, we explore the critical mathematical techniques that drive AI and ML research, focusing on key areas such as linear algebra, calculus, probability theory, and optimization. Linear algebra is fundamental to data representation and transformations, enabling algorithms to operate on high-dimensional datasets. Calculus plays a pivotal role in the optimization processes that underpin learning models, particularly in the training of neural networks and the application of gradient-based methods. Probability theory helps manage uncertainty in decision-making processes, as well as in model predictions, forming the basis of many AI methods, including Bayesian networks and Markov decision processes. Lastly, optimization provides the necessary techniques for model training and parameter tuning, which are critical to the development of efficient machine learning algorithms. This paper provides a comprehensive overview of these mathematical disciplines, offering insights into their application in modern AI and ML systems.

Keywords: Mathematics, Artificial Intelligence (AI), Machine Learning (ML), Mathematics Model