Spatiotemporal networks (networks whose topology and attributes change over time) play an important role in modern computer science – for modelling different types of real-life relationships (computer networks, social networks, etc.), for representation of rich data structures like texts or images, or just as a brick for modern model architectures like graph convolutional neural networks. This subsession is focused on data-driven learning of spatiotemporal network structures and discusses existing challenges and emerging approaches.
Data Science Fundamentals: this subsession is focused on application of machine learning models to solve real world problems, for example Markov-modulated linear regression and its application in transportation. It also addresses modelling, traffic simulation and examples of different real-world projects.