Entropy Randomization in Machine Learning presents a new approach to machine learning - entropy randomization - to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning, the book considers several applications to binary classification, modelling the dynamics of the Earth population, predicting seasonal electric load fluctuations of power supply systems, and forecasting the area of thermokarst lakes in Western Siberia.

Preface