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_ uniroma1.it)

 

Information

 

Course begins  September, 24 2018,

ends December 22.

 

Lesson timetable

Tuesday      15:00 - 18:00 - A17 - SPV

Wedneday    16:00 - 18:00  Axx - SPV

 


Tools  - FTP download

Links

International labs link

 

Course description

 

This course introduces neural networks 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; application on intelligent data analysis, patterns recognition, multi-sensors data-fusion, blind source separation.

 

The educational objectives include the acquisition of the following skills: 1) knowledge and understanding of the problems related to the use of NNs; 2) the ability to apply knowledge on NNs in the most common problems described in the course (knowledge and know-how), 3) development of independent judgment regarding the possible optimal solution with NNs of a given problem, 4) the development of communication skills on the topics covered in the course, 5) the ability to autonomous learning on specialized texts.  

 

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

  • Computational and biological inspired learning machines

  • Mathematical and Statistical Preliminary

  • Introduction to Adaptive Systems and Algorithms

  • Bayesian Approach to Adaptive Computation

  • Linear Adaptive Filtering 

  • Human Brain and Bio-inspired Intelligent Circuits

  • Feed Forward Multilayer Neural Networks

  • Kernel Methods and Regularized Neural Networks

  • Deep Neural Networks

  • Convolutive and Fully Recurrent Neural Networks



References

Text books and papers

 

Further reading books

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

  • Thomas Weise, “Global Optimization Algorithms Theory and Applications”, University of Kassel, http://www.it-weise.de/

  • 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.