# Supervised Learning (SL) Machine learning paradigm where models learn from labeled examples (input-output pairs). The model learns to map inputs to correct outputs by minimizing the difference between predictions and labels. Most common ML approach. Types: classification (discrete outputs) and regression (continuous outputs). Foundation of most practical ML systems. Contrasts with [[Unsupervised Learning]] (no labels) and reinforcement learning (reward signals). Rules of thumb: - Pretty much anything we can do with a second of thought can be automated with supervised learning - Can perform complex tasks that take hours/days or longer to perform for a human (e.g., market research) cfr [[What Machine Learning can and cannot do]] ## References - ## Related - [[Machine Learning (ML)]] - [[Deep Learning]] - [[Unsupervised Learning]]