Unlearning is About Removing Influences of Training Data Points

Machine unlearning involves removing influences of certain training data points from a model. This process aims to reverse the effects of specific training data on the model. It focuses on the idea of forgetting or discarding certain training examples for various reasons, such as privacy or accuracy improvement. Machine unlearning necessitates a nuanced approach for different applications, such as removing specific faces from a facial recognition model or sensitive content from a language model training data.