The study of removing the influence of specific training data from machine learning models without full retraining. Encompasses exact unlearning (retraining from scratch on retained data), approximate unlearning (efficiently producing models statistically close to retrained ones), and certified unlearning (providing formal guarantees via cryptographic-style indistinguishability bounds). Key applications include data privacy (GDPR “right to be forgotten”), bias removal, and correcting learned shortcuts.