University of  Rome  "La Sapienza"

DIET -  Department of Information Engineering, Electronics and Telecommunications  


Course Info: 

Neural Networks

Master (Laurea Magistrale) in: Artificial Intelligence and Robotics


Prof. Aurelio Uncini  (info: aurelio.uncini _AT_




Course begins  September, 26 2017,

ends December 22.


Lesson timetable

Tuesday      14:00 - 16:00 A4

Wedneday    9:00 - 12:00  A4



Room A4: Via Ariosto


Tools  - FTP download


International labs link


Course description


This course introduces neural networks and others soft computing methods which, unlike hard computing, are tolerant of imprecision, uncertainty and partial truth. Topics include: neural networks model, architectures, mathematical property and learning algorithms; optimization algorithms for soft computing methods; introduction to fuzzy-set; application on intelligent data analysis, patterns recognition, multi-sensors data-fusion, blind source separation.


The student acquires basic and specific knowledge related to the discipline. In particular, it is able to implement (and evaluate the performance) of complex systems for pattern recognition and dynamic data processing.


Final exam modalities


After the study of the course material and a preliminary discussion with the teacher, the examination consists in the discussion of an assigned home work.


The course will be taught in English


Course syllabus

  1. Computational and biological inspired learning machines

  2. Mathematical and Statistical Preliminary

  3. Introduction to Adaptive Systems and Algorithms

  4. Bayesian Approach to Adaptive Computation

  5. Linear Adaptive Filtering 

  6. Human Brain and Bio-inspired Intelligent Circuits

  7. Feed Forward Multilayer Neural Networks.

  8. Recurrent Neural Networks.

  9. Kernel Methods and Regularized Neural Networks

  10. Dynamic Stochastic Neural Networks

  11. Deep Neural Networks

  12. Adaptation by Random Search Algorithms and Soft computing


Text books and papers

  • A. Uncini, Introduction to Adaptive Algorithms and Machine Learning, Lecture notes ed. Sept. 2017. available (only) at A4Z copy center, (via della Polveriera, 13, 00184 Roma).

  • Li Deng and Dong Yu, Deep Learning Methods and Applications, Foundations and Trends in Signal Processing 7:3-4, 2014
  • Y. LeCun, Y. Bengio, G. Hinton, Deep Learning, Nature, May 2015


Further reading books

  • S. Haykin, “Neural Networks”, MacMillan College Publishing Company, NY, 2009.

  • Thomas Weise, “Global Optimization Algorithms Theory and Applications”, University of Kassel,

  • R.O. Duda e P.E. Hart, “Pattern Classification and Scene Analysis”, J. Wiley & Sons, 1973 (MAT 68-1973-03IN, ING2 EL.0069).

  • J.-S.R. Jang, C.-T. Sun, E. Mizutani, “Neuro-Fuzzy and Soft Computing”, Prentice Hall, 1997.

  • A. Uncini, Fundamentals of Adaptive Signal Processing - Springer, Febrary 2015.