The goal of the class is to present Artificial Neural Networks and other well known Machine Learning techniques (e. g. Gaussian Processes, Bayesian Learning, hidden Markov models, etc.) as systems for solving supervised and unsupervised learning problems, with a specific emphasis on Robotics applications. Such learning systems can be applied to pattern recognition, function approximation, time-series prediction and clustering problems. Some mention will be made to the use of ANNs as static systems for information coding, and dynamical systems for optimization and identification.
Ricevimento: Su appuntamento; disponibile anche senza appuntamento per questioni brevi
RENATO UGO RAFFAELE ZACCARIA (Presidente)
STEFANO ROVETTA (Presidente)