The SUPERVISED-learning setup gives the learner
ALabelled (x, y) pairs and asks the learner to predict y on new x
BOnly a reward signal, no inputs
CA graph structure with no node labels
DOnly inputs x without any labels
Answer & Solution
Correct answer: A. Labelled (x, y) pairs and asks the learner to predict y on new x
Supervised learning is parametrised by a labelled training set {(x⁽ⁱ⁾, y⁽ⁱ⁾)} and learns h(x) ≈ y.
Unsupervised has only x; reinforcement learns from a reward signal.
Keep practising Linear Regression and Supervised Learning
Browse all Linear Regression and Supervised Learning questions for Claude, or explore more Aifoundations topics.
Related questions
Split batch from stochastic descentThe resulting per-example log-likelihood is, up to constantsClosed-form normal equations require XᵀX to beLogistic regression uses which hypothesis function?The PROBABILISTIC justification for the least-squares cost assumes the noise ε⁽ⁱ⁾ in y⁽ⁱ⁾ The design matrix X for n training examples with d features (plus intercept) has shapeThe closed-form 'normal equations' solution for least-squares linear regression isThe LMS (Widrow–Hoff) update rule on a single training example is