The course aims to introduce students to the study and modelling of natural disasters using artificial intelligence techniques. After an initial introduction to the programming language used (Python), the student learns how to prepare a dataset and how to build, validate and optimise machine learning based models. The concepts of supervised, semi-supervised and unsupervised machine learning are introduced and the basics of building neural network-based models for image recognition (convolution neural network (CNN)) and signal analysis (recurrent neural network (RNN) ) are provided.
All datasets used during the laboratory phase are related to seismic and environmental risk assessment and mitigation.