|Labeled data with input-output pairs
|Unlabeled data with no explicit target labels
|Feedback-based learning through interactions
|Input features (X) and corresponding target labels (Y)
|Input features (X) without corresponding target labels
|Input features (X) and environment feedback (rewards and penalties)
|Make predictions or decisions on new data
|Discover patterns and relationships in data
|Learn a policy to make optimal decisions
|Image classification, sentiment analysis, regression tasks, etc.
|Clustering, anomaly detection, dimensionality reduction, recommendation systems,
|Game playing (e.g., AlphaGo), robotic control, self-driving cars, etc.
|Supervised learning algorithms optimize a mapping between X and Y using labeled data
|Unsupervised learning algorithms seek to find patterns or structure in the data without labels
|Model learns through trial and error with exploration and
|Requires labeled data for training
|Does not require labelled data
|Requires understanding of the environment and its feedback
|Performance measured based on prediction accuracy or other classification metrics
|Evaluation is more challenging and may be based on metrics like clustering quality
|Evaluation is based on long-term cumulative rewards and penalties
|Exploration vs Exploitation
|Balancing exploration and exploitation
|Linear regression, logistic regression, support vector machines, decision trees, etc.
|K-Means clustering, Gaussian Mixture Models, autoencoders, etc.
|Q-learning, Deep Q Network (DQN), Policy Gradient methods,Actor-Critic, etc.