Les principes fondamentaux de la théorie classique de l’information ont joué un rôle essentiel dans l’évolution de la révolution numérique actuelle. En effet, les notions d’entropie et les techniques qui leur sont associées sont au cœur de tâches essentielles liées à l’information, telles que la compression des données, la transmission de bits sur des liaisons filaires et sans fil, ainsi que les algorithmes d’apprentissage permettant d’extraire des informations. Les codes utilisés aujourd’hui dans les grands centres de données pour compresser les données, ou encore le codage multi-résolution des vidéos que nous diffusons sur des plateformes telles que YouTube, sont tous inspirés des idées fondamentales développées dans le domaine de la théorie classique de l’information.

Au cours des dernières décennies, les découvertes majeures de Deutsch, Grover et Bennett, culminant avec la remarquable découverte par Shor d’un algorithme de factorisation des nombres premiers, ont démontré que le traitement de l’information quantique (IQ) peut offrir des avantages sans précédent dans divers domaines du traitement de l’information.

Notre vision pour l’avenir est de concevoir un Internet quantique efficace, robuste et évolutif : un réseau de dispositifs capables de traiter et de communiquer de manière optimale des qubits. Forts de notre expérience dans la construction de réseaux classiques de communication de bits, comme illustré précédemment, il apparaît clairement que les avancées en théorie de l’information quantique nous permettront de concrétiser cette vision.

QUNIT sera le premier d’une série de deux cours consacrés à la présentation des concepts fondamentaux de la théorie de l’information quantique. Dans ce cours, nous nous concentrons sur les systèmes quantiques à un seul émetteur et un seul récepteur, également appelés systèmes point à point. Le deuxième cours portera sur les réseaux quantiques multi-terminaux, comprenant plusieurs nœuds émetteurs et/ou plusieurs nœuds récepteurs.

The proper treatment of modern communication systems requires the modelling of signals as random processes. Often the signal description will involve a number of parameters such as carrier frequency, timing, channel impulse response, noise variance, interference spectrum. The values of these parameters are unknown and need to be estimated for the receiver to be able to proceed.

Parameters may also occur in the description of other random analysis of communication networks, or in the descriptions of sounds and images, or other data, e.g. geolocation. This course provides an introduction to the basic techniques for estimation of a finite set of parameters, of a signal spectrum or of one complete signal on the basis of a correlated signal (optimal filtering, Wiener and Kalman filtering). The techniques introduced in this course have a proven track record of many decades. They are complementary to the techniques introduced in the EURECOM course Stat. They are useful for other application branches such as machine learning, in the EURECOM courses MALIS and ASI.

Teaching and Learning Methods: Lectures, Homework, Exercise and  Lab session (groups of 1-2 students depending on size of class).

Course Policies:  Attendance of Lab session is mandatory (15% of final grade).

« Those who fail to plan, plan to fail... ».  Architects, tailors, and directors all use plans (or models) for their creation, and software engineers are no exception. Thus, it is a common practice for software project managers to rely on the UML langage to document their software projects, and to perform modeling of the software itself. 

Teaching and Learning Methods : Lectures (20%), Exercises (40%), Lab sessions (40%) 

Course Policies: Attendance to  labs is mandatory

 

Course Description

The Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the web is defined. It derives from W3C director Sir Tim Berners-Lee's vision of the Web as a universal medium for data, information, and knowledge exchange.

First, this course provides a guided tour for a number of W3C recommendations allowing one to represent (RDF/S, SKOS, OWL) and query (SPARQL) knowledge on the web as well as the underlying logical formalisms of these languages, their syntax and semantics. Therefore, we present the problems of modeling ontologies and reconciling data on the web.

Second, this course offers an introduction to natural language processing and information extraction technologies with the goal of populating knowledge graphs. This includes the inner working of (large) language models, and their adaptation to ontology-guided information extraction tasks.

These two pillars constitute essential pieces in modern GraphRAG architecture, and in information systems that aim to support decision making while being grounded and explainable.

Teaching and Learning Methods: Lectures and Lab sessions (group of 2 students max)

Course Policies: Attendance to all sessions is mandatory.

Overview

Advanced Topics in Deep Learning is a short, intensive Master's-level course organized in the style of a research seminar or PhD reading group.

The primary objective of the course is to develop the ability to read, understand, critically assess, and discuss recent research papers in deep learning.

Students will be expected to engage directly with contemporary research literature and to develop technical ownership of one selected paper. The course is intended to provide an initial experience of the type of activity that is common in PhD research groups: reading difficult papers, reconstructing the relevant background, presenting technical material clearly, answering questions, and participating actively in scientific discussion.

Teaching and Learning Methods

The course is organized around sessions in which students will present cutting-edge research papers published in major conferences and journals in machine learning. In each session, some students will be presenting, while the others will have to follow and ask pertinent questions to deepen their understanding of the presented work.

Course Policies

Attendance to all sessions is mandatory. Presence sheets will be completed each time on Auriga

The aim of this course is to introduce students to physical and psycho acoustics, digital audio technologies, sound processing and synthesis techniques specific to live-sound, audio and music applications. Special emphasis is placed on practice with the support of audio-specific software.

Teaching and Learning Methods: The lecture is divided in half between the theoretical part, which is enriched by sound examples, and practice in the laboratory.

Course Policies: Attendance to lectures and labs is not mandatory but highly recommended.

Description

The course will cover:

• Physical and psycho acoustics;
• Fundamentals of digital audio;
• Techniques and technologies for sound analysis, processing and synthesis;
• Hands-on practice with dedicated audio deployment tools.

The detailed course programme can be viewed at this link: https://www.massimilianotodisco.eu/teaching.html

Learning outcome

Students will be able to:

• understand and identify the fundamental characteristics of sound for the physical and perceptual world;
• understand the principles of digital audio;
• select and implement established signal processing and synthesis methods for sound and music signals;
• develop and evaluate practical sound-based applications.

Bibliography

• Fletcher, N. H., & Rossing, T. D. (1991). The physics of musical instruments. New York, Springer-Verlag.
• Vaseghi, S. V. (2007). Multimedia Signal Processing: Theory and Applications in Speech, Music and Communications. J. Wiley.
• Everest, F. and Pohlmann, K. (2001). Master Handbook of Acoustics. 5th ed. New York, McGraw-Hill.
• Müller, M. (2015). Fundamentals of Music Processing - Audio, Analysis, Algorithms, Applications. Springer.
• Course slides.

Requirements: Proficiency in mathematics, physics and statistics.

Grading Policy: Exam (80%) + Lab test (20%)

Nb hours of lectures/labs: 10.5/10.5

Nb hours per week: 3

 This course covers a variety of topics, all related to the use and   management of a Linux operating system. In particular, the course   is divided in three parts dedicated respectively to the command-line,  to the Python programming language, and to maintaining, compiling, and  installing applications.

This course provides a broad introduction to cryptography and communication security mechanisms based on cryptography. The course covers fundamental aspects such as security evaluation criteria and the mathematical constructs underlying cryptographic primitives as well as applied aspects like the design of major encryption and hashing algorithms, details of security mechanisms relying on cryptography such as data encryption, integrity, digital signature, authentication,  key management, and public-key infrastructures.

Teaching and Learning Methods : Lectures and Lab sessions

Course Policies : Attendance to Lab sessions is mandatory.

Reinforcement Learning (RL) has recently emerged as a powerful technique in modern machine learning, allowing a system to learn through a process of trial and error using feedback. It has been succesfully applied in many use-cases, including systems such as AlphaZero, that learnt to master the games of chess, Go and Shogi.

The goal of this course is to introduce the students to basic concepts of RL such as, Markov decision processes, dynamic programming, model-based methods, approximation methods via value function and policy evaluation and many more useful tools.  This is a theoretical course but we will provide examples of real-world applications to demonstrate the usefulness of RL.

Teaching and Learning Methods

Each lecture starts summarizing key concepts from previous lecture. Part of each lecture is often dedicated to illustrative examples and exercises.

Course Policies

Attendance to lectures and exercise sessions is mandatory. Your presence is awarded with 10% of the overall grade.

Would you like to investigate beyond the surface of Windows, MacOS, Linux, Android? Fed up with not understanding the origin of segmentation faults, why you need to eject a USB key before physically removing it, or why/how your Android system can execute Pokemon Go and Facebook at the same time? You want to delve into the details of the inner workings of the Linux kernel? Join us to discover the power of Operating Systems!

Teaching and Learning Methods : Lectures (40%), Lab sessions (40%), Project (20%)

Course Policies: Attendance to some lab and project session is mandatory

This course explores optimization techniques with a focus on convex, non-convex optimization, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods, evolutionary algorithms, and reinforcement learning. The curriculum combines theoretical lectures with practical laboratories to equip students with both the knowledge and skills needed to apply optimization techniques in various fields. Teaching and Learning methods: Weekly lectures and Laboratories

The architectures of networks and service delivery platforms are subject to an unprecedented techno-economic transformation. This trend, often referred to as Network Softwarization, will yield significant benefits in terms of reducing expenditure and operational costs of next-generation networks. The key enablers are Network Function Virtualization (NFV), Software-Defined Networking (SDN), Cloud and Edge Computing.

This course will cover the principle of Network Softwerization by introducing and detailing the concepts of SDN, NFV and Cloud Computing (focusing on the IaaS model and Edge Computing). Besides covering the theoretical aspects, the course will provide an overview of the enabling technologies, and how combining these concepts will allow building flexible and dynamic virtual networks tailored to services. 

Teaching and Learning Methods: Lectures and lab. sessions. 

Course Policies: Attendance to Lab sessions is mandatory. 

The goal of this course is to introduce the students to the basic concepts of secure multiparty computation, the foundational MPC protocols and someadvanced blockchain protocols.  This is a theoretical course! We will see foundational aspects of MPC, protocols, proof (as in math) of security, fundational aspects of Blockchain.

Teaching and Learning Methods:Lectures and homework.

Course Policies: Final project and homework arenot mandatory

This course presents a series of mobile systems in their entirety to synthetize the knowledge gained in more fundamental courses. It explores current and emerging standards and follows the evolution of various mobile services.

Teaching and Learning Methods : Lectures and Lab sessions (group of 2 students)

Course Policies : Attendance to Lab session is mandatory.

This course will discuss all relevant aspects related to mobile systems security. Mobile devices have been revolutionized users' lives, and more than two billions mobile devices have been sold to date. Unfortunately, these devices, their operating systems, and the applications running on them are affected by security and privacy concerns. This course will be hands-on and will cover topics such as the mobile ecosystem, the design and architecture of mobile operating systems, rooting and jailbreaking, application analysis, malware reverse engineering, malware detection, vulnerability assessment, automatic static and dynamic analysis, and exploitation and mitigation techniques. While this course will mostly focus on Google's Android OS (its open nature makes it possible to have more interesting exercises and projects), it will also cover technical details about Apple's iOS as well.

Teaching and Learning Methods : Lectures , labs, and homework assignments.

Course Policies : Class and lab attendance is not checked but generally required to succeed.

This course aims to present a treatment of mathematical methods suitable for engineering students who are interested in the rapidly advancing areas of signal analysis, processing, filtering and estimation. Significant current applications relate to, e.g., speech and audio, music, wired and wireless communications, instrumentation, multimedia, radar, sonar, control, biomedicine, transport and navigation.  The course presents a study of linear algebra,  probability, random variables, and analogue systems as a pre-requisite to material relating to sampled-data systems.  Time permitting, the final part of the course covers the concepts of random processes, the analysis of random signals, correlation and spectral density.

Teaching and Learning Methods: The course is comprised of lectures, exercises and laboratory sessions.

Course policies: This course is aimed at students who have NOT already completed preparatory classes.  Completion of all in-lecture examples is strongly advised.