Which of the following is NOT a type of machine learning?

Distributed learning is not classified as a type of machine learning in the same way that the other options are. Instead, distributed learning typically refers to a framework or approach for training machine learning models across multiple machines or systems to enable scalability and efficiency.

In contrast, supervised learning involves training a model on labeled data, where the output is known, enabling the algorithm to learn the relationship between input and output. Deep learning is a subset of machine learning that utilizes neural networks to learn from data, often comprising multiple layers to model complex relationships. Unsupervised learning, on the other hand, deals with unlabeled data and aims to uncover patterns or groupings without pre-existing labels.

Thus, while distributed learning is related to the process of scaling and improving efficiency in machine learning, it does not represent a distinct type of learning methodology like the others listed. This differentiation is what makes the choice of distributed learning as the correct response to the question.