Master (Laurea Magistrale) in: Artificial Intelligence and Robotics
Course begins September, 25 2019,
ends December 22.
Wednesday 9:00 - 13:00 - A41 - SPV
Wednesday 17:00 - 19:00 A33 - SPV
Tools - FTP download
International labs link
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 course will be taught in English