Machine Learning

Machine learning is a subfield of artificial intelligence focused on developing algorithms and models that let computers learn and make predictions or decisions without being explicitly programmed. It sits at the intersection of computer science, mathematics, and statistics, and it is how modern systems identify patterns and trends inside data.

Three ways to learn

Supervised learning works from labeled data with known input-output pairs, using regression and classification methods such as linear regression. Unsupervised learning finds patterns in unlabeled data through techniques like clustering and dimensionality reduction, including principal component analysis. Reinforcement learning places an agent in an environment where it earns rewards or penalties and optimizes its actions over time, often framed as a Markov decision process.

Why statistics matter

Statistical methods are the infrastructure beneath all of it — the tools that analyze the structure of data, quantify uncertainty, and let a model make trustworthy predictions on points it has never seen. By combining these mathematical and computational tools, machine learning turns large datasets into decisions, surfacing relationships that would otherwise stay hidden.