A.Y. 2023-24 – Digital Signal Processing – 1st Year II semester (6 cfu)

A.Y. 2023-24 – Digital Signal Processing – 1st Year II semester (6 cfu)

Students attending Digital Signal Processing (DSP) classes (6 CFU for Mechatronics Engineering) are kindly invited to enroll on Delphi for virtual classroom registration. Please inform the teaching secretariat if you have any problems. Please inform the didactic secretariat of any issues.

Digital Signal Processing (block C1-C2-opt)

Deep Learnig and applications (block C1-opt)

2 YEAR II semester  6 CFU

Eugenio Martinelli
A.Y. 2024-25 new
Didatticaweb
Code:
SSD: ING-INF/01

PREREQUISITES

Basic knowledge of probability theory, signal theory, and pattern recognition.

FORMATIVE OBJECTIVES

LEARNING OUTCOMES:
Learning the basic concepts of deep learning algorithms. The main Machine Learning algorithms will be covered, followed by a focus on those related to deep learning, with particular emphasis on their application in the field of mechatronics.

KNOWLEDGE AND UNDERSTANDING:
The student acquires knowledge related to the field of Machine Learning, with particular reference to the ability to extract quantitative and qualitative information from images and videos and multivariate data and their subsequent processing for regression and classification tasks.

APPLYING KNOWLEDGE AND UNDERSTANDING:
The student acquires the capability to implement the algorithms in Matlab through dedicated lessons during the course to the aim of being able to autonomously develop new codes for the solution of specific problems in different application fields.

MAKING JUDGEMENTS:
The student must be able to integrate the basic knowledge provided with those deriving from the other courses, such as probability, signal theory, and pattern recognition.

COMMUNICATION SKILLS:
The student develops a project in Matlab that illustrates during the oral exam. The project can be done in groups to demonstrate working group capabilities.

LEARNING SKILLS:
Students will need to be able to read and understand scientific texts and articles in English for in-depth exploration of the topics covered. They should also independently expand their knowledge of the subject to include topics not directly addressed in the course, particularly those connected with the rapid technological developments in the field of Deep Learning and, more generally, in machine learning.

SYLLABUS

Today, deep neural networks surpass traditional hand-crafted algorithms and match human performance in various complex tasks, including image recognition, natural language processing, and prediction models. This course offers a comprehensive introduction to neural networks (NNs), covering traditional feedforward (FFNN) and recurrent (RNN) neural networks, as well as the most advanced deep-learning models like convolutional neural networks (CNN), Variational Autoencoders, and Diffusion models.

The primary objective of the course is to equip students with the theoretical knowledge and practical skills needed to understand and utilize neural networks (NN), while also familiarizing them with deep learning techniques for solving complex engineering challenges.
This goal is pursued in the course by:
• Describing the most important algorithms for NN training (e.g., backpropagation, adaptive gradient algorithms, etc.)
• Illustrating the best practices for successful training and using these models (e.g., dropout, data augmentation, etc.) in a practical session using a phyton environment.
• Providing an overview of the most successful Deep Learning architectures (e.g., convolutional networks, autoencoder for embedding, diffusion models, etc.)
• Providing an overview of the most successful applications with particular emphasis on models for solving visual recognition tasks.

TEXTS

Pattern recognition and machine learning, Christopher Bishop.

Deep Learning, Ian Goodfellow et al.

– slides of the professor

 

 

ERASMUS International Dinner 14 December 2023 h: 19:00

ERASMUS International Dinner 14 December 2023 h: 19:00

Thursday, 14 December h. 7.00pm at the School of Economics

We are happy to announce that, after a three-year break, the Erasmus+ International Dinner is back: Everyone is welcome to this event, an international dinner with Erasmus students, international students, and national students from Tor Vergata University of Rome, to be held on Thursday, December 14, 2023.

Read more

CLICI – Italian language courses 2023/2024

Clici

Courses in Italian Language for foreign students – A.Y. 2023-24 

CLICI organizes Courses in Italian Language and Culture for Tor Vergata University’s foreign students and for external participants. The Courses (60 hours each) will be run from the second half of March 2024 to June 2024.  

The final exams of the courses will be held by June 2024. 

 

http://clici.uniroma2.it/en/courses/courses-in-italian-language-for-tor-vergata-foreign-students/

 

CLICI contacts:

Clici Office: Campus X, Via di Passolombardo 341, 00133 Roma

Email: info.linguaitaliana@uniroma2.it

Site: http://clici.uniroma2.it

Tel. +39 06 7259 91027

The University of Central Florida’s College of Engineering and Computer Science seeks applications for its PhD

The University of Central Florida’s College of Engineering and Computer Science seeks applications for its PhD

The University of Central Florida’s College of Engineering and Computer Science seeks applications for its PhD for the Fall 2024 and Spring 2025 admissions cycles. Approximately 92% of our PhD students are funded through fellowships, research assistantships, and teaching assistantships. Prospective students can filter through our collection of available research positions at the following webpage:

https://grad.cecs.ucf.edu/prospective-students/research-positions/

 

Over 100 positions are available. Because of your connection to students at University of Rome Tor Vergata, it would be great if you could share this with individuals who might be interested in continuing their engineering or computer science studies in Orlando, FL, USA.

The application process begins here: https://grad.cecs.ucf.edu/prospective-students/applying/. Questions can be referred to me at ali@ucf.edu. I appreciate your help.

 

Many Thanks,

 

Ali P. Gordon, Ph.D., F. ASME (he/him/his)

Professor and Associate Dean for Graduate Affairs (OGA)

College of Engineering and Computer Science (CECS), University of Central Florida (UCF)

ali@ucf.edu

Transportation

Transportation

By METRO and BUS

Take metro A to the Anagnina terminus and then bus 20, which passes through the various points of Tor Vergata. Alternatively, you can take line C at a stop at Torre Angela, but reaching it from there is quite complicated.

Bus serivce Tor Vergata (private)

https://web.uniroma2.it/it/percorso/utilitr_e_servizi/sezione/servizio_bus_navetta

 

 

Navetta A: from Monday to Friday, connections with the Tor Vergata railway station (via Fermi – Municipality of Frascati) and the Macroarea of Sciences MFN, passing through the various Macroareas/ Faculty;

Navetta B: from Monday to Friday (first run at 7.30 AM and last run at 05:40 PM), connects the Metro Station A Anagnina with Campus X/ CLA, passing through the university and stopping at the Metro C Torre Angela.

Erasmus+ 2021-2027 programme

Erasmus+ 2021-2027 programme

IMPORTANT INFORMATION: For the Erasmus+ 2021-2027 programme, Learning Agreements must be managed online. Higher Education Institutions can do this by using the Online Learning Agreement platform or an equivalent system connected to the Erasmus Without Paper Network. Therefore, this template is provided by the European Commission for information purposes only and must not be used to manage Learning Agreements for studies. Please visit the Erasmus Without Paper Competence Centre for a more detailed data standard, to which all equivalent systems need to adhere. For further guidance on how to manage Online Learning Agreements – Please read the Guidelines on how to use the Learning Agreement for studies.