I seguenti corsi sono stati erogati per il Dottorato Nazionale in Intelligenza Artificiale in Sapienza.
Per maggiori informazioni consultare il link allegato alla pagina.
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Title: Scrittura tecnico-scientifica (9^a edizione)
Instructor(s): Emilio Matricciani
Title: Game-Theoretic Approach to Planning and Synthesis
Instructor(s): Giuseppe De Giacomo, Antonio Di Stasio, Giuseppe Perelli, Shufang Zhu
Title: Non-Markov Decision Processes and Reinforcement Learning
Instructor(s): Giuseppe De Giacomo, Luca Iocchi, Fabio Patrizi, Alessandro Ronca, Roberto Cipollone, Gabriel Paludo Licks, Elena Umili
Title: Uncertainty and Probability
Instructor(s): Andrea Messina, Giulio D’Agostini
Title: NETWORKED CONTROL SYSTEMS AND SECURITY
Instructor(s): Mauro Franceschelli
Title: Artificial Intelligence
Instructor(s): Giorgio Fumera
Title: Machine Learning
Instructor(s): Battista Biggio
Title: Machine Learning Security
Instructor(s): Battista Biggio
Title: Biometric Technologies and Behavioural Security
Instructor(s): Gian Luca Marcialis
Title: Effective habits and skills for successful young scientists
Instructor(s): Fabio Roli
Title: Adversarial Machine Learning
Instructor(s): Fabio Roli
Title: Mobile Security
Instructor(s): Alessio Merlo, Luca Verderame –
Title: Cognitive and conversational agents
Instructor(s): Viviana Mascardi
Title: High Performance Deep Learning with GPUs
Instructor(s): Sergio Orlandini (CINECA), Giuseppe Fiameni (NVIDIA)
Title: Trustworthy and Privacy-Aware Data Analysis
Instructor(s): Andrea Saracino
Title: Learning and Indexing Visual Representations
Instructor(s): Giuseppe Amato, Fabio Carrara, Fabrizio Falchi, Claudio Gennaro (ISTI - CNR)
Title: [AI4CTIA] Cyber Threat Intelligence Analysis and how Artificial Intelligence can Support It
Instructor(s): Federico Cerutti
Title: Diritto e Giustizia dell’Intelligenza Artificiale
Instructor(s): Simona Cacace (docente referente)
Title: Declarative Problem-solving with Answer Set Programming
Instructor(s): Prof. Francesco Ricca and Prof. Simona Perri
Title: Argumentation e Intelligenza Artificiale
Instructor(s): Prof. Sergio Greco
Title: Approximate Computing for low-energy, high-speed and area-efficient digital circuits and systems: when “good enough” is better than “good”.
Instructor(s): Ing. Fabio Frustaci
Title: Hardware for Deep Learning
Instructor(s): Prof. Adam Teman (Bar-Ilan University, Ramat Gan, Israel)
Title: Digital technologies and artificial intelligence law (italian)
Instructor(s): Dr. Pasquale Laghi
Title: Quantum Computing and its Application to Machine Learning
Instructor(s): Ing. Carlo Mastroianni, Prof. Francesco Plastina
Title: Data Stream Mining
Instructor(s): Prof. João Gama (Univ. Di Porto)
Title: Modelling and mining multilayer networks
Instructor(s): Prof. Matteo Magnani (Uppsala University)
Title: Deep Learning and Statistical Learning
Instructor(s): Dr. Giuseppe Manco
Title: Statistical data analysis and signal processing techniques for imaging and non-destructive testing
Instructor(s): Prof. Marco Ricci, Dr. Stefano Laureti
Title: Binary Analysis with Applications to Machine and Deep Learning
Instructor(s): Prof. Antonella Guzzo, Ing. Michele Ianni
Title: Active Learning
Instructor(s): Prof. Sergio Flesca, Ing. Eugenio Vocaturo
Title: Numerical Computations on the Infinity Computer
Instructor(s): Ing. Marat Mukhametzhanov
Title: Lipschitz Global Optimization
Instructor(s): Prof. Yaroslav Sergeyev
Seminars
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Title: Digital Medicine Seminars
Digital Epidemiology
Organizers: Ciro Cattuto, Sebastiano Filetti, Stefano Leonardi
Epidemiological models: Paolo Villari (Sapienza)
Digital Epidemiological models: Daniela Paolotti (ISI Foundations)
Digital tracking: Luca Ferretti (Oxford)
Contact Networks: Ciro Cattuto (Università di Torino)
Digital surveillance systems: Caterina Rizzo (OPBG)
Outbreaks, Epidemics, and Epidemiological Data Analysis: Patrizio Pezzotti (ISS)
Diabetes Webinar
Organizer: Marianna Maranghi.
Title: Deep Learning Seminars (3 CFU)
Organizers: Simone Scardapane, Fabrizio Silvestri
The three seminars will cover several advanced topics in deep learning: meta learning (i.e., “learning to learn”), continual learning (i.e., learning from a continuous stream of tasks), and data engineering for deep learning (i.e., preparing data for being used in deep learning pipelines).
Meta-learning: Dr. Vincenzo Lomonaco (Pisa University)
Continuous learning: Dr. Vincenzo Lomonaco (Pisa University)
Data engineering: Andreas Damianou (Spotify, tentative)
Data engineering: Andreas Damianou (Spotify, tentative)
Title: Cultural Analytics (3 CFU)
Organizers: Carlos Castillo and Giorgio Barnabò
Wikipedia beyond the encyclopedic value: Diego Saez-Trumper (Wikimedia)
Wikipedia beyond the encyclopedic value: Diego Saez-Trumper (Wikimedia)
Cultural Analytics in Python: Chris Danforth and Peter Dodds (Vermont University)
Goodreads, a computational Study: Melanie Walsh (University of Washington)
Media Content Analysis and Culturomics: Nello Cristianini (Bristol University)
Media Content Analysis and Culturomics: Nello Cristianini (Bristol University)
Title: Foundations of Deep Learning (3 CFU)
Organizers: Fabrizio Silvestri, Michael Bronstein
Geometric Deep Learning: Petar Velickovic (DeepMind & Cambridge Univ.)
Complex Systems and DNN: Lenka Zdeborova (EPFL Lausanne)
Theory of Neural Tangent Kernel: Sanjeev Arora ? (Princeton Univ.)
Causality and Deep Learning: Bernard Scholkopf ?(ETH Zurich)