Mathematical Foundations and Applications in Machine Learning

Abstract

Machine learning has developed into a powerful tool for forecasting and drawing inferences from large amounts of information. Machine learning algorithms are based on mathematical concepts to build models, discover patterns and generate accurate predictions. This paper investigates the application of mathematics in various machine learning models and methods. It illustrates how several mathematical fields work together to develop useful machine learning methods. Creating an algorithm that suggests a model that can learn from data and make predictions. It is used to understand why a model is superior to another and why something works. An inherent mathematical prerequisite is machine learning. Calculus, probability, linear algebra and statistics all work together to power machine learning.