PERIODO: NOVEMBRE - GENNAIO 2017

Il corso sarà tenuto dal prof Giansalvo CIRRINCIONE dell'Universitè de Picardie

In recent years neural computing has emerged as a practical technology, with successful applications in many fields. The majority of these applications are concerned with problems in pattern recognition, and make use of feed-forward network architectures such as the multi-layer perceptron and the radial basis function network. Also, it has also become widely acknowledged that successful applications of neural computing require a principled, rather than ad hoc, approach. This course provides a more focused treatment of neural networks than previously available, which reflects these developments. By deliberately concentrating on the pattern recognition aspects of neural networks, it has become possible to treat many important topics in much greater depth. For example, density estimation, error functions, parameter optimization algorithms, data pre-processing, and Bayesian methods are each the subject of a lesson. From the perspective of pattern recognition, neural networks can be regarded as an extension of the many conventional techniques which have been developed over several decades. Indeed, this course includes discussions of several concepts in conventional statistical pattern recognition which are essential for a clear understanding of neural networks.
This course is aimed at researchers in neural computing as well as those wishing to apply neural networks to practical applications. It is also intended to be used as an advanced course on neural networks.

venerdì 18 novembre 2016, e avrà luogo in Aula C del DET, piano seminterrato, sede di Corso Montevecchio, secondo il seguente calendario:

18/11/16 9-12
21/11/16 10-13
28/11/16 10-13
02/12/16 9-11,30
12/12/16 10-13
16/12/16 9-12
19/12/16 10-13
09/01/17 10-13
13/01/17 9-11,30
16/01/17 10-13
20/01/17 9-11,30
23/01/17 10-13
27/01/17 9-11,30
30/01/17 10-13