Third edition of Artificial Intelligence: foundations of computational agents, Cambridge University Press, 2023 is now available (including the full text).
7.9 Review
The following are the main points you should have learned from this chapter:
- Learning is the ability of an agent improve its behavior based on experience.
- Supervised learning is the problem that involves predicting the output of a new input, given a set of input-output pairs.
- Given some training examples, an agent builds a representation that can be used for new predictions.
- Linear classifiers, decision trees, and Bayesian classifiers are all simple representations that are the basis for more sophisticated models.
- An agent can choose the best hypothesis given the training examples, delineate all of the hypotheses that are consistent with the data, or compute the posterior probability of the hypotheses given the training examples.
