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, 25 2019,

ends December 22.

 

Lesson timetable

Wednesday       9:00 - 13:00 - A41 - SPV

Wednesday    17:00 - 19:00  A33 - 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

 

The exam consists of a discussion of an assigned  project or home-work (max 24pt) and some theoretical questions (max 6pt). 

 

The home-work is assigned to the student in the last week of the course, and typically the student can choose the project from a list of possible topics.

 

The project  can also be done by a group of maximum 3 students. In this case the task of each individual student must be well specified.


The final discussion of the project is done at the teacher's office by appointment (at any period of the year, and usually Tuesday afternoons). For the final exam the student must: 1) present a report (in the form of a short scientific paper) on the project carried out; 2) give a short presentation  in which the results and the acquired skills are highlighted.

 

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

  • Human Brain and Bio-inspired Intelligent Circuits

  • Feed Forward Multilayer Neural Networks

  • Kernel Methods and Regularized Neural Networks

  • Deep Neural Networks

  • Convolutional and Fully Recurrent Neural Networks



References

Text books and papers

  • A. Uncini, Introduction to Neural Networks and Deep Learning, Lecture notes ed. 2019.

  • A. Uncini, Mathematical Elements for Machine Learning lecture notes, 2019.

 Other other recommended text 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.