 Machine learning, in the sense given  above,  has been associated with probabilistic techniques. Probabilistic techniques have been associated with both the learning of functions (e.g. Naive Bayes classification) and the modeling of theoretical properties of learning algorithms. For example, a standard reformulation of supervised learning casts it as a Bayesian problem. Assume that we are looking at recognizing digits \([0{-}9]\) from a given image. One way to cast this problem is to ask what the probability that the hypothesis \(H_x\): “the digit is \(x\)” is true given the image \(d\) from a sensor. Bayes theorem gives us: 