  During its first 2 years of mission the Fermi-LAT instrument discovered more
than 1,800 gamma-ray sources in the 100 MeV to 100 GeV range. Despite the
application of advanced techniques to identify and associate the Fermi-LAT
sources with counterparts at other wavelengths, about 40% of the LAT sources
have no a clear identification remaining "unassociated". The purpose of my
Ph.D. work has been to pursue a statistical approach to identify the nature of
each Fermi-LAT unassociated source. To this aim, we implemented advanced
machine learning techniques, such as logistic regression and artificial neural
networks, to classify these sources on the basis of all the available gamma-ray
information about location, energy spectrum and time variability. These
analyses have been used for selecting targets for AGN and pulsar searches and
planning multi-wavelength follow-up observations. In particular, we have
focused our attention on the search of possible radio-quiet millisecond pulsar
(MSP) candidates in the sample of the Fermi-LAT unidentified sources. These
objects have not yet been detected but their discovery would have a formidable
impact for our understanding of the MSP gamma-ray emission mechanism.
