Elective in AI gives 12 credits and Research Topics in AI gives 6 credits within the
Master in Artificial Intelligence and Robotics
Elective in AI is structured in the four modules described below, while Research Topics in AI is composed by two out of the four modules. Any two modules from Elective in AI can be taken to cover Research Topics in AI (6CFU), provided that Elective in AI has not been included in the study plan.
Please refer to each module, following the links in this page for additional details.
Professor in charge of exam registration is Prof. Christian Napoli (https://cnapoli.diag.uniroma1.it/).
All information are provided in the web sites of the modules.
Each module has specific modalities and deadlines. Please check the module web sites.
Exam dates:
Unless stated differently in each module website, the following dates represent deadlines for submission of the material needed for the evaluation of projects:
June 11, 2024
July 15, 2024
September 01, 2024
October 14, 2024
January 13, 2025
February 17, 2025
June 02, 2025
July 07, 2025
September 01, 2025
October 06, 2025
January 13, 2026
Projects delivered by any of these dates will be evaluated in 3 weeks.
If this is your last exam before graduation, make sure you submit the evaluation material well in advance to avoid last-minute procedures
For registration of Elective in AI exam, after completing all the modules, send an e-mail to the Professor in charge (c.napoli@uniroma1.it with subject of email starting with [EAI]) providing the following information: name, matricola, list of given exams (title of the module, professor of the module, and grade).
The registration will be completed within 3 weeks of the above deadlines, provided all the modules have been completed (i.e., the vote is agreed with each Professor), the email requesting for registration arrives at least 2 days before the OFFICIAL EXAM DATE on infostud, and the grades have been confirmed by the professors, for each modules (the latter is an internal procedure that does not involve the students).
Total grade for 12 CFU is computed as follows:
- 30 with Laude in each module is counted as 31
- all the votes are weighted averaged
- the final grade is rounded to the closest integer (0.5 is rounded to the highest value, e.g. 29.5 -> 30)
- Laude is granted with weighted average >= 30.5 (e.g., 30 L x 6 CFU + 30 x 6 CFU -> 30 L)
Prof. Christian Napoli
6 CFU - 1st Semester
As computer science, assistive technologies and robotics evolve towards application fields in which humans cooperate with machines, working closer and closer, the requirements for human computer interactions increase. Visual perception is an important component for human–machine interaction processes in all kinds of computer systems. Interaction between humans and computers depends on the reliability of the perception systems, and, above all, the vision system. The analysis of activities, motions, skills, and behaviors of humans and robots are generally addressed by using the features of a moving human body (or body part). The human motion behavior is then analyzed by body movement kinematics, and the trajectory of the target is used to identify the objects and the human target. The process of human target identification and gesture recognition in a quite non-trivial problem. In this series of lectures we will focus on the context of Human-Robot Interaction (HRI) along with the related problems on the field of vision and perception, applied to robotic systems. We will devise the typical characteristics of vision and perception related hardware device, as well as the relative software systems and solutions. We will explore the known approaches characterizing well known visual recognition systems, as well as the most important algorithmic solutions for people targeting and body parts recognition. A theoretical and practical framework will be given with several example. Finally we will discuss the state of the art on human-centric vision analysis and explain the importance of the matter relatively to human-based interfaces of computer/robots with special interest in human motion and activity recognition. We will also devise several tracking systems and motion oriented context and object recognition techniques, with emphasis on deep learning techniques applied to visual recognition. Finally we will compare the applicability of such techniques to human motion classification and the related application on the field of Human-Computer Interaction.
Prof. Luca Iocchi
3 CFU - 2nd Semester
Applications involving robots interacting with people are gaining increasing interest. The course will provide an overview of recent methods and techniques for Human-Robot Interaction and Social Robotics. Several interaction modalities will be discussed in the course, including: vision, speech, body motion, user interfaces, etc.
Classroom code: cwy56jw
Prof. Vincenzo Suriani
3 CFU - 2nd Semester
This course provides students with a foundation in robotic skill development in vision control, motion, and behavior programming to integrate artificial intelligence techniques with robotics. It covers benchmarking robotic systems and introduces semantic reasoning using ontologies and knowledge graphs, enabling robots to understand and interact with their surroundings. It covers techniques for image and scene interpretation, as well as 3D scene reconstruction for practical robotic applications.
Classroom code: cwy56jw
Prevoius years
3 CFU - 2nd Semester
One of the most important goals of Artificial Intelligence concerns the development of intelligent agents, such as robots, that are able to deliberate their course of actions. This course introduces several approaches to realize this goal, with a particular focus on advanced forms of automated behaviour synthesis, based on temporal logics and action theories, in both deterministic and non-deterministic settings.
Students are encouraged to realize projects which integrate such high-level forms of reasoning, with state-of-the-art probabilistic robotics, computer vision, and machine learning tools.
where: Room B2 - Via Ariosto 25
when: Monday, Tuesday, Wednesday (8.30-10.00), starting September 25th
3 CFU - 1st Semester
The field of Natural Language Processing (NLP) has witnessed unprecedented growth and innovation in recent years, making it one of the most dynamic and exciting domains in artificial intelligence. This course will explore the most current and groundbreaking developments in NLP, offering students a deep dive into the latest advancements, techniques, and challenges that define the field today, including:
Transformer Models and Beyond: understanding the impact of models like BERT, GPT-3, and their successors
Low-Resource and Cross-Lingual NLP: exploring strategies for NLP in languages with limited resources and in cross-lingual applications
Multimodal NLP: investigating the integration of text with other modalities such as images and audio
Event extraction: automatically identifying and extracting structured information about events, such as dates, locations, participants, and their roles, from unstructured text data
Dialogue: exploring some challenges of conversational AI.
Commonsense knowledge and reasoning: addressing the challenges of identifying and encoding commonsense information and reasoning with it
Summarization: introducing abstractive and extractive methods, and their applications in distilling information from vast textual datasets
Interpretable NLP: sketching some ideas behind making NLP models interpretable and transparent, and its importance