This thesis is devoted to the re-implementation of a very large scale kernel ridge regression software tool and to its extension to the semi-supervised case Started: November 2018 Candidate: Enrico Civitelli Graduated: April 2019
Managing inventories when the demand is uncertain is a challenging problem involving forecasting, modeling and optimization. This thesis is devoted to the extension of classical models, like the newsvendor, to multiple products, integer order quantities, logical constraints, assortment, and other
Analytics has found its place in most sports. This thesis is devoted to analyze some publicly available tennis data and try to extract significant information by means of machine learning. (image credits: https://www.ubitennis.com/blog/2015/04/22/tennis-e-statistiche-avanzate-le-cause-di-un-rapporto-difficile/)
We would like to experiments with optimization methods applied to DNN’s in order to obtain a sparse trained network, i.e. a network with a small number of neurons or connections.
This thesis is devoted to experiment numerical optimization techniques on the D-Wave quantum computer We will simulate some combinatorial optimization problems and try to find innovative problem mapping in order to fully exploit the capacity of the quantum annealing machine.
How to use information collected during past matches in order to be able to predict the final result of a match? This thesis explores machine learning methods and features extraction towards the goal of being able to anticipate the final result.
Despite the progress of medicine in recent decades, epilepsy is a disease whose knowledge is still limited. About one third of patients with this disease continue to present crises despite pharmacological treatments, surgical interventions, and the medical assistance. The most
Starting from the paper “A novel method for the identification of conserved structural patterns in RNA: From small scale to high-throughput applications”, by Marco Pietrosanto, Eugenio Mattei, Manuela Helmer-Citterich,Fabrizio Ferrè, Nucleic Acids Research, 2016 1, doi: 10.1093/nar/gkw750, we wish to explore
by Cosimo Casini Planning the optimal trajectory of an interplanetary space mission allows to save large amounts of fuel and time. Such a task can be modelled as a constrained global optimization problem. However, solving the resulting model is extremely
We would like to look inside some classical statistical procedures (e.g., forecasting time series) from an optimization point of view. The aim is to use global optimization techniques to solve optimal estimation problems in statistics