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Differentiate between supervised, unsupervised, and reinforcement learning in Artificial Intelligence

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Differentiation Based on Supervised Learning Unsupervised Learning Reinforcement Learning

Features The training set has both predictors and predictions. The training set has only predictors. It can establish state-of-the-art results on any task.


Linear and logistic regression, support vector machine, and Naive Bayes

K-means clustering algorithm and dimensionality reduction algorithms Q-learning, state-action-reward-state-action (SARSA), and Deep Q Network (DQN)

Uses Image recognition, speech recognition, forecasting, etc. Preprocessing data, pre-training supervised learning algorithms, etc. Warehouses, inventory management, delivery management, power system, financial systems, etc.

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Supervised learning is a system in which both input and desired output data are provided. Input and output data are labeled to provide a learning basis for future data processing.

Unsupervised learning procedure does not need labeling information explicitly, and the operations can be carried out without the same. The common unsupervised learning method is cluster analysis. It is used for exploratory data analysis to find hidden patterns or grouping in data.

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