Machine Learning (ML)

Level 1

Teaching a computer by showing it lots of examples instead of telling it exactly what to do.

Level 2

A way computers learn patterns from data instead of following hard-coded rules — like how you get better at a video game by playing more.

Level 3

A subset of AI where algorithms improve their performance on tasks by learning from data rather than being explicitly programmed.

Level 4

Statistical methods that enable systems to learn from data, identify patterns, and make decisions with minimal human intervention. Includes supervised, unsupervised, and reinforcement learning paradigms.

Level 5

The study of algorithms that optimize a performance criterion using example data or past experience — encompassing PAC learning, empirical risk minimization, kernel methods, and deep parametric function approximation.

Sources

• NIST Trustworthy and Responsible AI Resource Center: Glossary — National Institute of Standards and Technology. Accessed 2026-07-20.