Overview and Supervised Learning

(14th-May-2020)

Learning is the ability of an agent to improve its behavior based on experience. This could mean the following:

1. The range of behaviors is expanded; the agent can do more.

2. The accuracy on tasks is improved; the agent can do things better.

3. The speed is improved; the agent can do things faster.

• Now we considers supervised learning:

• given a set of training examples made up of input-output pairs, predict the output of a new input.

• We show how such learning may be based on one of four possible approaches: choosing a single hypothesis that fits the training examples well, predicting directly from the training examples, selecting the subset of a hypothesis space consistent with the training examples, or finding the posterior probability distribution of hypotheses conditioned on the training examples.