Foundations and paradigms of machine learning

Machine learning definition and data generalization

Machine learning (ML) is a field of artificial intelligence focused on developing statistical algorithms that learn from data and generalize to new, unseen data. This means ML systems can perform tasks without being explicitly programmed for each specific case. The foundations of ML come from statistics and mathematical optimization, and many common ML methods can be framed as minimizing an empirical risk objective. Deep learning, a major approach within ML, uses neural networks and has achieved strong performance on many tasks. A core objective of a learning system is generalization: the ability to make accurate predictions on new examples after training on a finite dataset. Because the training data is drawn from an unknown probability distribution and future data is uncertain, learning theory often provides probabilistic performance bounds rather than exact guarantees. Generalization quality is closely related to model complexity: overly simple models can underfit, while overly complex models can overfit and generalize poorly. Computational learning theory studies feasibility (e.g., polynomial-time learnability) and theoretical limits, including frameworks such as probably approximately correct (PAC) learning.

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Deep learning as a machine learning subset

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