Sapienza - University of  Rome

DIET -  Department of Information Engineering, Electronics and Telecommunications  


Course Info: 

Machine Learning

Prof. Aurelio Uncini   (info: aurelio _._ uncini _AT_ uniroma1 _._ it)

Laurea Magistrale: Ing. Comunicazioni, Ing. Informatica, Ing. Elettronica


Possible prosecution for Thesis, (also in companies, or in collaboration with other research centers at foreign universities).
For information contact the Teacher or its collaborators
 Dr. Simone Scardapane,  Dr. Michele Scarpiniti, Dr. Danilo Comminiello.


Procedures for the final examination:
The exam consists of an oral pre-test on the whole program and a dissertation (home-work project) that relates to a specific part of the program.




Course beginning:

Febr. 26, 2020
Duration 13 weeks.


Lesson timetable


10:00 - 14:00

Room 20, SPV


08:00 - 12:00

Room 20, SPV








The main objectives of the course “Machine Learning” ( ML) is to introduce students to state-of-the-art methods and basic programming tools for analysis of complex and big-data set. The ML course provides theoretical and practical basic-tools for the study and determination of adaptive and machine learning algorithms: linear, nonlinear, supervised, unsupervised for static (or memoryless) applications: regression, classification and clustering; and dynamic application: adaptive filtering, modeling and prediction of complex physical phenomena. In particular, are provided: the theoretical and practical basic-tools for linear adaptive filtering in different application; the formal statistical methods for performance analysis of ML algorithms and the basic tools for the algorithm development on vector and parallel architectures and large-scale distributed environment. A secondary objective of the ML course  is to teach students how to develop, and evaluate, simple ML or adaptive filtering algorithms in various application domains such as: multimedia and multimodal communications, biological, biomedical, sound, telecommunications, remote sensing, social networks, internet, big-data, industry 4.0,  etc. Therefore, every student will lead a project that is based on machine learning state-of-the-art research.

Objectives of the Course

The student acquires the basic theory and is able to design, implement and evaluate the performance of, most common machine learning algorithms, also on parallel machines with different grain on several application contexts.

Main topics

Review of Mathematical Elements for Machine Learning


Introduction to Adaptive Computation and Machine Learning


Bayesian Approach to Adaptive Computation


Basic Supervised and Unsupervised Algorithms for Pattern Recognition, Regression and Clustering


Linear Adaptive Filtering: Wiener Theory, LMS algorithm and its variants  


Solution of Underdetermined Systems of Linear Equations with Minimal L1- L2- Norm


Shallow and Deep Neural Networks


Convolutional Neural Networks


Generative Adversarial Networks


Recurrent Neural Networks


Kernel Methods and SVM





  • Aurelio Uncini, “Introduction to Neural Networks and Deep Learning”, Ed. 2020 (free pdf available  for the students). 
  • Aurelio Uncini, “Mathematical Elements for Machine Learning”, Ed.  2020 (free pdf available  for the students).
  • Aurelio Uncini, "Fundamentals of Adaptive Signal Processing" - Springer, ISBN 978-3-319-02806-4, Feb. 2015  S. Scardapane, D. Comminiello, M. Scarpiniti, A. Uncini, “Designing Lerge Machine Learning Simulations Using the Lynx Toolbox".

Other recommended reading

  • Ia Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", MIT Press book, Ed 2018.
  • A. Zhang, Z. C. Lipton, M. Li, A. J. Smola,  "Dive into Deep Learning",, march, 2020.
  • Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”,  Adaptive Computation and Machine Learning series, MIT Press book
  • Sergios Theodoridis, “Machine Learning: A Bayesian and Optimization Perspective”, 1st Edition - Elsevier 2015.

Old historical books

  • Rumelhart, D.E., Hinton, G.E., & McClelland, J.L. (1986). “A General Framework for Parallel Distributed Processing”, In Rumelhart, D.E., & McClelland, J.L. and the PDP Research Group (1986) Eds. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations. MIT Press: Cambridge, MA.