 Another recent resurgence in neurocomputational techniques has occurred in machine learning. The modus operandi in machine learning is that given a problem, say recognizing handwritten digits \(\{0,1,\ldots,9\}\) or faces, from a 2D matrix representing an image of the digits or faces, a machine learning or a domain expert would construct a feature vector representation function for the task. This function is a transformation of the input into a format that tries to throw away irrelevant information in the input and keep only information useful for the task. Inputs transformed by \(\rr\) are termed features. For recognizing faces, irrelevant information could be the amount of lighting in the scene and relevant information could be information about facial features. The machine is then fed a sequence of inputs represented by the features and the ideal or ground truth output values for those inputs. This converts the learning challenge from that of having to learn the function \(\ff\) from the examples: \(\left\{\left\langle x_1, \ff(x_1)\right\rangle,\left\langle x_2, \ff(x_2)\right\rangle, \ldots, \left\langle x_n, \ff(x_n)\right\rangle \right\}\) to having to learn from possibly easier data: \(\left\{\left\langle \rr(x_1), \ff(x_1)\right\rangle,\left\langle \rr(x_2), \ff(x_2)\right\rangle, \ldots, \left\langle \rr(x_n), \ff(x_n)\right\rangle \right\}\). Here the function \(\rr\) is the function that computes the feature vector representation of the input. Formally, \(\ff\) is assumed to be a composition of the functions \(\gg\) and \(\rr\). That is, for any input \(x\), \(f(x) = \gg\left(\rr\left(x\right)\right)\). This is denoted by \(\ff=\gg\circ \rr\). For any input, the features are first computed, and then the function \(\gg\) is applied. If the feature representation \(\rr\) is provided by the domain expert, the learning problem becomes simpler to the extent the feature representation takes on the difficulty of the task. At one extreme, the feature vector could hide an easily extractable form of the answer in the input and in the other extreme the feature representation could be just the plain input.