ADVANCES OF MACHINE LEARNING: A SURVEY OF METHODS, BENCHMARKS, MODELS AND DATASETS IN INDUSTRY APPLICATIONS

Abstract

This study explores the fundamentals of Machine Learning (ML), a sub-discipline of Artificial Intelligence (AI) that enables systems to learn and make decisions based on new data without explicit programming. It provides an introduction to the various types and approaches to ML, highlighting the models' ability to improve over time as they process more data. Additionally, the paper traces the historical evolution of ML, from statistical learning theory to neural networks, and examines its relevance in modern society, driven by the availability of big data and advances in computational power. Furthermore, the study investigates the application of ML across major industries such as healthcare, finance, and retail, demonstrating its potential to solve complex problems, enhance decision-making processes, and transform industries.