Developing efficient shortest path algorithm in large urban graphs, with multiple, conflicting, objectives and different modes of transportation. Skills required: C++
Python Global optimization for space trajectories
Numerical experiments with global optimization for space trajectory planning, using a library developed at ESA Candidate: Tommaso Aldinucci Graduated: april 2017
Global Optimization methods for semi-supervised learning
A quasi-Newton based approach to train a semi-supervised Support Vector Machine. Candidate: Lorenzo Norcini – graduated Feb 2017
Machine Hearing
content-based feature engineering for automatic identification of musical genre Candidate: Tina Raissi
Operations Research courses @ Florence rank #1
In the main Computer Science Engineering curricula at Florence University, Operations Research courses rank #1: Fondamenti di Ricerca Operativa (prof. Fabio Schoen): #1 course for the Laurea degree in Computer Science Engineering Optimization Methods (prof. Marco Sciandrone)
Semi-supervised training via a lagrangean approach
Implementation of global optimization algorithms, preferably in python, to trai a SVM in which some of the data has no label Many algorithms exist for S3VM – exact (branch and bound) and heuristic (global optimization). We aim at implementing some
Forecasting time series with Support Vector Regression
A comparison between classical (ARIMA) forecasting methods for time series and regression based on Support Vector Machines. A huge set of economic time series is available to train and validate foreasting methods Skills required: basic computer science skills; python might
Do not waste past computations: Learning and Optimization for Optimal Circle Packing
A simple task: use machine learning in order to avoid useless local searches to be started. Try to find new putative optimal configurations learning from past trials. Skill required: just python – most numerical experiments in circle packing have already been
Semi supervised learning by continuous optimization methods
Training an SVM when many (most) data are unlabeled. This thesis considers an approach based on a continuous differentiable formulation of the problem. Candidate: Andrea Boddi Start: January 2016 Image credits: http://inverseprobability.com/ncnm/
Predictive Mainteinance
Changed to machine learning for ephylectic seizure prediction candidate: Alberto Pitti Machine learning for fault prediction in mechanical engines