Learning A Improves performance of future tasks after observing the world

Regression B Representation of a system or process that can be used to make predictions or decisions

Decision Boundary C Agent learns patterns in the input with no explicit feedback

Overfitting D Agent observes input$$output pairs & learns to map input to output

Cross$$validation E Example input$$output pairs, from which to discover a hypothesis

Unsupervised Learning F Examples distinct from training set, used to estimate accuracy

Model G Supervised learning with a discrete set of possible output values

Supervised Learning H Supervised learning with numeric output values

Test Set I Surface in a high$$dimensional space that separates the classes

Training Set J Randomly split the data into a training set and a test set

Classification K Choose an over$$complex model based on irrelevant data patterns