 Like cases of perceptual learning, machine learning can be either supervised or unsupervised, although these distinctions mean something very specific in the machine case. In supervised learning, builders test the machine’s initial performance on, say, the recognition of whether a given image contains a face. They then measure the performance error and adjust the parameters of the machine to improve performance (LeCun, Bengio, &amp; Hinton 2015: 436). Importantly, in cases of supervised learning, engineers program into the machine which features it should look for when, say, identifying a face. In cases of unsupervised learning, by contrast, the machine does not have information about its target features. The machine merely aims to find similarities in the given images, and if it is successful, the machine comes to group all the faces together according to their similarities (Dy &amp; Brodley 2004: 845).