Information

Course begins  September, 23 2020,

ends 18 December .

Lesson timetable

Wednesday      9:00 - 13:00 - A11, SPV

Wednesday    17:00 - 19:00  - A5, SPV

Tools  - FTP download

• SqEduNN_Matlab 

Links

• Società Italiana Reti Neuroniche
• Research Index CiteSeer
• SPIB
• Journal of Machine Learning Research
• Trans. on Neural Networks
• ICA Central
• TC Blind Signal Processing  IEEE CAS Society
• TC Machine Learning for Signal Processing IEEE SP Society

International labs link

• Blind Singal Processing Links

Course description

This course introduces the Deep Neural Networks (DNN) architectures, the relative (deep) learning algorithms, and their (main) applications

The educational objectives include the acquisition of the following skills: 1) knowledge and understanding of the problems related to the use of DNNs; 2) the ability to apply knowledge on DNNs in the most common problems described in the course (knowledge and know-how), 3) development of independent judgment regarding the possible optimal solution with DNNs 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 with Bayesian Approach

• Human Brain and Bio-inspired learning machines

• Shallow and Deep Neural Networks

• Recurrent Neural Networks (RNNs) and Gated Neural Networks

• Convolutional Neural Networks

• Generative Neural Networks

• Kernel Methods and Regularized Neural Networks

References

Text books and papers

• A. Uncini, Introduction to Neural Networks and Deep Learning, Lecture notes + slides - ed. 2020.

• A. Uncini, Mathematical Elements for Machine Learning, Lecture notes + slides - ed.  2020.

 Other other recommended text and papers

• I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT book E. 2018. 

• Aurélien Géron, Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow, 2nd Edition, Published by O’Reilly Media, 2019 

• Li Deng and Dong Yu, Deep Learning Methods and Applications, Foundations and Trends in Signal Processing 7:3-4, 2014

 

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.