Machine Learning for Signal Processing

A.Y. 2023/2024

The course will start on Thursday, March 7, 2024.

General Information
Credits: 6 CFU
Scientific sector: ING-IND/31
Course language: English
Offered programs: Laurea Magistrale in Ingegneria Elettronica (LMIE) and Master Degree in Electronics Engineering (MDEE). The course is also commuted for the Laurea Magistrale in Ingegneria delle Comunicazioni (MCOR). However, the course can be also freely chosen and attended by interested students from other degree programs of Sapienza University, upon approval of the study plan.
Classroom page: Download slides, videolectures, Python notebooks, homework, and additional material. Access code: cyqsj25.
Office hours: by appointment
Calendar: February 29 - May 31, 2024
Class timing:
  - Thursday 8:30-10:00, Aula 32, Via Eudossiana 18
  - Friday 8:30-10:45, Aula 23, Via Eudossiana 18

Course Description
Information conveyed in real-world signals may be often affected by noise, partially corrupted or even unavailable. Thus, extracting desired information from real-world signals can be even very complicated. Machine learning for signal processing (MLSP) is the science that deals with the development of efficient algorithms and models that are able to detect and unveil a possible hidden structure in signals, thus recovering a desired information. This process is autonomously and automatically performed by MLSP algorithms, by simply learning from the available data, which is the basis of any science related to artificial intelligence.

This course aims at presenting the main machine learning and deep learning paradigms and applying them for the processing of a variety of signals, including electrical and mechanical signalss, sensor signals, audio and speech, images, movies, music, biological and medical signals, among many others. The course is based on regular classroom lessons, which also include regular exercises in Python on practical problems.

Most of the prerequisites will be briefly recalled in classes. However, basic knowledge of linear algebra, signal processing theory and stochastic processes are warmly recommended, as well as basic programming skills.

Exam Assessment
Exam grades (in thirties) will be based on a project and oral questions. The theoretical skills acquired by the student will be evaluated as well as the ability to apply and implement a specific methodology in a practical problem. Lode is given only to students presenting an outstanding project and theoretical preparation.


Detailed program

Textbook and Material

Main textbooks:

Further references:

Supplementary material (e.g., course slides, papers) will be provided by the instructor.

Exam dates

Students are reminded that exams are booked electronically via the INFOSTUD portal. Booking for extraordinary exam sessions is allowed only to part-time students, students enrolled in supplementary years or non-conventional paths, and graduating students.

Scheduled exam sessions for the year 2022/2023:

About Machine Learning for Signal Processing

Here is some video by the IEEE Signal Processing Society about typical MLSP applications.

In the next video Prof. Bernard Widrow introduces ADALINE and talks about a "machine that learns from its own experience".