Learning how to use classical supervised and unsupervised machine learning algorithms by grasping the underlying computational and modeling issues.
Basic probability, calculus, algorithms.
The Course covers the basic ideas behind statistical learning and a number of prototypical supervised approaches, including, local methods, regularization networks, linear and non linear models. The Course also cover basic unsupervised problems such as clustering and dimensionality reduction. Special effort is devoted to discussing how to set up a repliable machine learning pipelines.
Ricevimento: Ricevimento su appuntamento da concordare via email (nicoletta.noceti@unige.it)
NICOLETTA NOCETI (Presidente)
ELENA NICORA
LORENZO ROSASCO (Presidente Supplente)
ALESSANDRO VERRI (Supplente)
