In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g., stock price prediction. Online learning algorithms may be prone to catastrophic interference, a problem that can be addressed by incremental learning approaches. In the setting of supervised learning, a function of is to be learned, where is thought of as a space of inputs and as a space of outputs, that predicts well on instances that are drawn from a joint probability distribution on . In reality, the learner never knows the true distribution over instances. Instead, the learner usually has access to a training set of examples . In this setting, the loss function is given as , such that measures the difference between the predicted value and the true value . The ideal goal is to select a function , where is a space of functions called a hypothesis space, so that some notion of total loss is minimised. Depending on the type of model (statistical or adversarial), one can devise different notions of loss, which lead to different learning algorithms. In statistical learning models, the training sample are assumed to have been drawn from the true distribution and the objective is to minimize the expected "risk" A common paradigm in this situation is to estimate a function through empirical risk minimization or regularized empirical risk minimization (usually Tikhonov regularization).