Deep Learning for Signal and Image Processing

A.Y. 2020/2021
Course offered to students on the PhD programme in
Visit the PhD website for the course schedule and other information
Overall hours
Lesson period
December 2020
Lead instructor: Angelo Genovese
The course will present recent artificial intelligence and machine learning techniques for multi-dimensional signal processing and pattern recognition, with a specific focus on Deep Learning (DL) approaches. With respect to traditional pattern recognition algorithms, DL methods have the advantage of automatically extracting distinctive data representations from multidimensional signals, thus reducing the need of domain expertise in a specific field. Currently, DL approaches represent the state of the art in several fields, such as industrial monitoring, medical imaging, biometric recognition, object classification, and ambient intelligence. However, the choice of the best DL model for a specific application is still a challenging design aspect. The course will present an overview on the main DL approaches for signal and image processing, such as Convolutional Neural Networks, Autoencoders, and Generative Adversarial Networks. Then, the course will present application examples for heterogenous scenarios, including industrial monitoring and ambient intelligence.
Image processing; Neural networks.
Assessment methods
Giudizio di approvazione
Assessment result
superato/non superato
How to enrol


The course enrolment deadline is usually the 25th day of the month prior to the start date. More specifically:

  • 25 February 2022 for courses starting in March 2022
  • 25 March 2022 for courses starting in April 2022
  • 26 April 2022 for courses starting in May 2022
  • 25 May 2022 for courses starting in June 2022
  • 27 June 2022 for courses starting in July 2022
  • 31 August 2022 for courses starting in September 2022.

How to enrol

  1. Access enrolment on PhD courses online service using your University login details
  2. Select the desired programme and click on Registration (Iscrizione) and then on Register (Iscriviti)

Ignore the option "Exam session date” that appears during the enrolment procedure.


For help please contact [email protected]

Appointment via e-mail
Office 6002 (6 floor)