Università Roma Tre

The course aims to provide advanced and specific skills in the area of the latest Deep neural network architectures. Particular attention will be given to multimodal models, and networks capable of analyzing complex data structures, such as graphs and multivariate time series; and deep reinforcement learning. At the end of the course, the student will be able to: adequately design and optimize Deep neural networks, be able to distinguish and evaluate different solutions, and be able to select and customize the most effective network architectures to be used in real application domains, supervised, unsupervised, or following a reinforcement learning approach. The course consists of a theoretical and methodological part on advanced and innovative concepts, and a laboratory activity in which these concepts are applied in problem solving using the latest development frameworks.