Machine learning approaches focus on the development of systems capable of learning and inferring from data to solve an application problem without being explicitly programmed with a set of step-by-step instructions from input to output. Learning refers to the computational process of optimizing from data the parameters of the model, which is a mathematical construct generating an output based on input data. The range of problems addressed by machine learning typically involves tasks for which other approaches fail, either because there is no suitable formalisation of the problem, or because the resolution of the problem is intractable with non-learning approaches. Machine learning approaches include for instance supervised, unsupervised and reinforcement learning, using a variety of methods including deep learning with neural networks, statistical techniques for learning and inference (including for instance logistic regression, Bayesian estimation) and search and optimisation methods.