The concept of Machine Learning (ML) originates from computer science and statistics, with foundational work by Arthur Samuel (1959) and later developments by Tom M. Mitchell and Geoffrey Hinton. It is based on the idea that algorithms can identify patterns and make decisions using data. The field has since evolved into multiple branches—supervised, unsupervised, and reinforcement learning—forming the computational foundation of predictive analytics and generative models.

Machine Learning refers to a collection of algorithms and statistical methods that allow computers to recognize patterns, adapt to new information, and make predictions or classifications without human intervention.

In ML, models are trained on historical data to detect relationships and optimize future outcomes. Common techniques include decision trees, neural networks, clustering, and regression analysis.

Within management and marketing, ML enables real-time personalization, demand forecasting, churn prediction, and dynamic pricing. It bridges data science and decision-making, transforming intuition-driven strategies into measurable, adaptive systems.

Recent advances—especially deep learning—have extended ML’s reach to complex domains such as image recognition, natural language understanding, and generative content creation.

E-commerce platforms use ML to recommend products based on past behavior, while financial institutions deploy it to detect fraud or assess credit risk. In sustainable management, ML optimizes energy consumption, resource allocation, and ESG performance tracking.