Machine Learning Paradigms: From Learning to Unlearning

Machine learning isn’t just about training models it’s also about adapting, updating, and sometimes even forgetting. Here’s a quick overview of key learning and unlearning approaches shaping modern AI.

1. Exact Unlearning

Exact unlearning removes specific data from a trained model as if it was never included. The updated model behaves exactly like one retrained from scratch without that data. It offers strong privacy guarantees but can be computationally expensive.

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2. Approximate Unlearning

Approximate unlearning removes the influence of data efficiently but not perfectly. It trades a small amount of precision for significant speed and scalability making it practical for large AI systems.

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3. Online Learning

Online learning updates the model continuously as new data arrives. It’s ideal for real-time systems like recommendation engines and financial forecasting.

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4. Incremental Learning

Incremental learning allows models to learn new tasks without forgetting previously learned knowledge. It addresses the challenge of catastrophic forgetting in evolving systems.

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5. Transfer Learning

Transfer learning reuses knowledge from one task to improve performance on another. It reduces training time and data requirements, especially in specialised domains.

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6. Federated Learning

Federated learning trains models across decentralised devices without sharing raw data. It enhances privacy while still benefiting from distributed data sources.

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7. Supervised Learning

Supervised learning uses labeled data to train models for classification and regression tasks. It’s the most widely used learning approach in industry.

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8. Unsupervised Learning

Unsupervised learning discovers hidden patterns in unlabeled data. Common applications include clustering and dimensionality reduction.

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9. Reinforcement Learning

Reinforcement learning trains agents through rewards and penalties. It powers game AI, robotics, and autonomous decision-making systems.

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10. Active Learning

Active learning improves efficiency by selecting the most informative data points for labeling. It reduces annotation costs while maintaining performance.

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11. Self-Supervised Learning

Self-supervised learning generates labels from the data itself. It has become foundational in modern large language and vision models.

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Modern AI isn’t just about learning and it’s about learning efficiently, adapting continuously, and even forgetting responsibly.