Dictionary · Russell_and_Norvig

Deep learning is a broad family of techniques for machine learning in which hypotheses take the form of complex algebraic circuits with tunable connection strengths. The word “deep” refers to the fact that the circuits are typically organized into many layers, which means that computation paths from inputs to outputs have many steps. Deep learning is currently the most widely used approach for applications such as visual object recognition, machine translation, speech recognition, speech synthesis, and image synthesis; it also plays a significant role in reinforcement learning applications.

An adaptive dynamic programming (or ADP) agent takes advantage of the constraints among the utilities of states by learning the transition model that connects them and solving the corresponding Markov decision process using dynamic programming.

A learning model that summarizes data with a set of parameters of fixed size (independent of the number of training examples)

[a machine learning algorithm that can] decide what actions to take [with regards to its training data, in contrast to a passive learning agent, which is limited to a fixed policy].

A passive learning agent has a fixed policy that determines its behavior. An active learning agent gets to decide what actions to take.

An outlier is a data point that is far from other points.

A means of learning a model and a reward function from observations that then uses value or policy iteration to obtain the utilities or an optimal policy; makes optimal use of the local constraints on utilities of states imposed through the neighborhood structure of the environment.