Answer: (ii) supervised learning

Explanation:

• Supervised learning uses labelled training data i.e. each input $x$ is paired with correct output $y$. The algorithm learns a mapping $f: X \rightarrow Y$ to predict labels for unseen data. Examples: classification (spam detection), regression (house price prediction).
• Unsupervised learning uses unlabelled data to find hidden structure (clustering, dimensionality reduction, association).
• Reinforcement learning uses reward/feedback from environment through trial-and-error, not explicit correct labels.
• Active learning is a special case of semi-supervised where learner queries an oracle for labels of selected instances — it uses labels but the base definition asked is supervised.

Therefore labelled training data is the defining characteristic of supervised learning.