(19th-May-2020)
• For an agent to use its experiences, the experiences must affect the agent's internal representation. Much of machine learning is studied in the context of particular representations (e.g., decision trees, neural networks, or case bases). This chapter presents some standard representations to show the common features behind learning.
Online and offline
In offline learning, all of the training examples are available to an agent before it needs to act. In online learning, training examples arrive as the agent is acting.
An agent that learns online requires some representation of its previously seen examples before it has seen all of its examples. As new examples are observed, the agent must update its representation. Typically, an agent never sees all of its examples.
Active learning is a form of online learning in which the agent acts to acquire useful examples from which to learn. In active learning, the agent reasons about which examples would be useful to learn from and acts to collect these examples.
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