Multiple instance learning (MIL) is a type of machine learning approach where the training data is arranged into bags, with each bag containing multiple instances. The goal of MIL is to learn a classifier that can make predictions at the bag level rather than the individual instance level. This is particularly useful in scenarios where the labels for the individual instances are ambiguous or uncertain, but the overall bag label is known. MIL has applications in various fields such as image classification, object detection, and drug discovery, where the input data can be represented as bags of instances. Researchers in this area are constantly working to develop new MIL algorithms that can effectively handle the complexity and ambiguity of bag-level learning tasks.