Machine unlearning is the subfield of ML that deals with the problem of making a model "forget" the influence of a given subset of samples used for training -- the so called "forget set". The need to unlearn arises, for example, in case a subject withdraws consent for data to be used to train ML models. The natural solution is to retrain the model without the forget set, but that may be impractical for very large models.