Definition: A machine learning approach where the model is trained using unlabeled data, without input-output pairs provided.
Better definition: When your computer becomes a rebellious teenager and learns without anyone telling it what to do.
Where does this fit in the AI Landscape?
Unsupervised learning is crucial for discovering hidden patterns in data and is often used in tasks like clustering and anomaly detection. It's particularly useful when labeled data is scarce or when exploring data for new insights. Unsupervised learning techniques are employed across various industries, including finance, cybersecurity, and retail.
What are the real world impacts of this?
nsupervised Learning techniques are used in areas like customer segmentation, anomaly detection, and content recommendation, helping businesses offer personalized experiences and improve operations. For developers, unsupervised learning offers unique ways to extract insights from data and tackle complex AI problems.
What could go wrong in the real world with this?
An unsupervised learning model is tasked with organizing a music library but gets so carried away that it creates entirely new genres, like polka-dubstep and reggae-fusion.
Could be used to find hidden patterns or structures in data, like grouping similar types of code snippets or chat messages.