# Machine Learning Lesson of the Day – Supervised and Unsupervised Learning

The 2 most commonly used and studied categories of machine learning are supervised learning and unsupervised learning.

• In supervised learning, there is a target variable, $Y$, and a set of predictor variables, $X_1, X_2, ..., X_p$.  The goal is to use $X_1, X_2, ..., X_p$ to predict $Y$.  Supervised learning is synonymous with predictive modelling, but the latter term does not connote with learning from data to improve performance in future prediction.  Nonetheless, when I explain supervised learning to people who have some background in statistics or analytics, they usually understand what I mean when I tell them that it is just predictive modelling.
• In unsupervised learning, there are only predictor variables and no target variable.  The goal is to find interesting patterns in $X_1, X_2, ..., X_p$.  This is a much less concretely defined problem than supervised learning.  Unsupervised learning is sometimes called pattern discovery, pattern recognition, or knowledge discovery, though these are not commonly agreed upon synonyms.