This means modifying or adding extraneous information to a training data set so that an algorithm learns harmful or undesirable behaviours.

Supervised learning starts with training data that are tagged with the correct answers (target values). After the learning process, you wind up with a model with a tuned set of weights, which can ...

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.