Unsupervised learning is a type of machine learning where a model is trained on data without labeled outcomes. The system analyzes and identifies patterns or structures in the data, such as clustering or associations, without explicit guidance on what to look for. It is often used for tasks like anomaly detection, clustering, and dimensionality reduction.
Observation in the context of learning refers to the process of acquiring knowledge, skills, or behaviors by perceiving the actions of others or the dynamics within an environment. This learning occurs without direct engagement but through experiencing of external stimuli, events, or behaviors.
No direct guidance :: Learner L must identify patterns or relationships from raw input on her own.
Pattern discovery :: Observation often involves recognizing and understanding patterns in the environment, which mirrors what happens in unsupervised learning.
Learning from the environment :: L derives insights from the world or data without external labels or supervision.