Real-World Applications of Anomaly Detection : Detecting the Unexpected Through Distributional Modelling
In many machine learning tasks, the premise is designed around predetermined targets and clear expectations of model behaviour. In such cases, there is a direct definition of the optimal mappings between inputs and outputs, which can be learned given sufficiently sized datasets and models. However, in many real-world scenarios, tasks are often not as well-posed and instead defined around detecting
