Which of the following is a type of learning approach in Machine Learning?

Unsupervised learning is a type of learning approach in machine learning that aims to find hidden patterns or intrinsic structures in input data that does not have labeled responses. In this approach, the algorithm is given a dataset without any explicit instructions on what to do with it, meaning that there are no predefined outcomes the model is supposed to predict. Instead, the model works to uncover the underlying structures or groupings within the data on its own.

This method is particularly useful in scenarios where it is difficult or costly to obtain labeled data. Common applications of unsupervised learning include clustering, anomaly detection, and dimensionality reduction, where the model identifies similarities or differences between data points to form groups or reduce the complexity of the data while retaining important information.

The other options do not reflect established approaches in machine learning. Reactive learning and dynamic learning are not widely recognized frameworks within the field, while independent learning might suggest a form of self-directed learning, but it does not specifically pertain to a clear category of machine learning methodology. Therefore, unsupervised learning stands out as a recognized and fundamental learning approach in the realm of machine learning.