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Artificial Intelligence Tutorial• 6 min read
Machine Learning Paradigms: Supervised, Unsupervised & RL
Understanding the core mechanisms of data-driven learning
Machine learning algorithms fall into three fundamental learning paradigms depending on the presence and structure of feedback signals.
The Three Main Branches of ML
1Supervised Learning: The algorithm is trained on labeled pairs
(X, y). The objective is to learn a mapping function f(X) -> y. Common tasks include Classification (discrete labels, e.g., Spam vs Not Spam) and Regression (continuous values, e.g., predicting house prices).2Unsupervised Learning: The model receives unlabeled inputs
X and discovers latent structure, hidden patterns, or clusters without explicit ground truth. Common tasks include Clustering (K-Means, DBSCAN) and Dimensionality Reduction (PCA, t-SNE).3Reinforcement Learning (RL): An autonomous agent interacts with a dynamic environment through actions, receiving scalar rewards or penalties to learn an optimal decision policy.
| Paradigm | Input Data | Feedback Mechanism | Typical Applications |
|---|---|---|---|
| Supervised | Labeled pairs (Features, Target) | Direct error metric (Loss gradient) | Fraud detection, medical imaging, churn prediction |
| Unsupervised | Unlabeled features only | Reconstruction loss, cluster dispersion | Customer segmentation, anomaly detection, PCA |
| Reinforcement | States, Actions, Transitions | Delayed scalar reward signal | Robotics, game-playing (AlphaGo), autonomous vehicles |
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Test Yourself With Exercise
Which machine learning paradigm groups customer purchase histories into cohorts without pre-existing labels?