Which machine learning technique does NOT rely on labeled datasets?

Unsupervised learning is indeed the technique that does not rely on labeled datasets. In unsupervised learning, the algorithm is tasked with finding patterns or structures in data that has not been labeled or categorized. This approach is commonly used to discover hidden features or groupings within data without prior guidance.

The goal is to analyze the data's distribution and to identify relationships among variables. This is particularly useful in fields such as clustering, anomaly detection, and density estimation, where insights can be drawn from unlabeled data.

In contrast, supervised learning requires labeled datasets to train algorithms, enabling them to learn how to map inputs to outputs based on provided examples. Similarly, reinforcement learning involves an agent learning how to make decisions based on feedback from its environment. While neural networks can be used in various contexts, including supervised and unsupervised learning, they typically require labeled data when applied to supervised tasks.