Machine Learning for Engineers

As discussed so far in this book, the standard formulation of machine learning makes the following two basic assumptions: 1. Statistical equivalence of training and testing. The statistical properties of the data observed during training match those to be experienced during testing – i.e., the population distribution underlying the generation of the data is the same during both training and testing. 2. Separation of learning tasks. Training is carried out separately for each separate learning task – i.e., for any new data set and/or loss function, training is viewed as a new problem to be addressed from scratch.