Available courses

[WebSem] Semantic Web and Information Extraction technologies (TRONCY, Raphaël)
Raphael Troncy

[WebSem] Semantic Web and Information Extraction technologies (TRONCY, Raphaël)

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. This course is a guided tour for a number of W3C recommendations allowing 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. We will present the problems of modeling ontologies and reconciling data on the web. Finally, we will explain how to extract knowledge from textual documents using natural language processing and information extraction technologies.

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

Course Policies:Attendance to Lab session is mandatory.

[TraffEEc] Emission and Traffic  Efficiency (HÄRRI, Jérôme)
Jerome Haerri

[TraffEEc] Emission and Traffic Efficiency (HÄRRI, Jérôme)

(Course for Post Master ITS  and International Master students only).

 

This module addresses mechanisms and strategies to improve traffic efficiency and carbon footprint. The objectives are first to introduce the underlying theory such as Waldrop Equilibrium required for efficient path planning. Second, it describes concepts and theory behind the optimization of traffic lights to traffic conditions. Third, it provides guidelines and methodologies to model emissions and integrate them into efficient path planning.  Finally, it trains on best practices to apply these concepts for efficient and green path planning on vehicular traffic simulators.

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

Course Policies : Attendance to Lab session is mandatory

[Speech] Speech and audio processing (EVANS, Nicholas)
Nicholas Evans

[Speech] Speech and audio processing (EVANS, Nicholas)

This course provides an introduction to the automatic processing of speech and audio signals.  It starts with a treatment of the human speech production and perception mechanisms and looks at how our understanding of them has influenced attempts to process speech and audio signals automatically.  The course then considers the analysis, coding and parameterisation of signals in the case of different speech and audio processing tasks.  After an introduction to essential pattern recognition techniques, the course considers specific applications including speech recognition, speaker recognition and speaker diarization.  The course also includes a treatment of speech and audio coding, noise compensation and speech enhancement.

Teaching and Learning Methods:

The course is comprised of lectures and exercises and laboratory sessions.

Course policies: Attendance of laboratory sessions is mandatory.

[SP4COM] Signal Processing for Communications (SLOCK, Dirk)
Dirk Slock

[SP4COM] Signal Processing for Communications (SLOCK, Dirk)

The subtitle of this course could be “Multi-Antenna Interference Handling for Multi-User Multi-Cell Systems”. Indeed the main focus is on the exploitation of multiple antennas to (more easily) handle inter-symbol and inter-user interference. Key concepts here are beamforming, MIMO (Multi-Input Multi-Output), Multi-User MIMO, Massive MIMO.

After a basic course in digital communications, a wide range of issues arise in the treatment of physical layer procedures in a wide variety of transmission technologies such as xDSL, gigabit Ethernet, powerline systems, DAB/DVB broadcasting and optical communication systems to name a few. These issues involve e.g. multi-rate echo cancellation for full duplex operation on twisted pair telephone lines, synchronization and equalization techniques in a variety of single and multi-carrier systems, impulsive noise in powerline and automotive systems etc. Even just wireless communications encompass a wide range of systems such as satellite, underwater, near-field communications, fixed wireless access, private systems, sensors, IoT, etc. and a wide range of aspects such as relaying, full duplex radio, cognitive radio, location estimation etc.

Whereas these systems will be briefly mentioned, the main focus will be on cellular wireless and the use of multiple antennas at receivers and transmitters. Spatial filtering, spatiotemporal filtering, and multiuser detection for CDMA are all treated in a unified fashion.

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

Course Policies: Attendance of Lab session is mandatory (25% of final grade).

[Radio] Radio engineering (KALTENBERGER, Florian)
Florian Kaltenberger

[Radio] Radio engineering (KALTENBERGER, Florian)

This course treats the subject of modern radio engineering and includes typical RF architectures and their characterizations, modeling, prediction and simulation of radio-wave propagation, cellular planning, systems-level aspects of modern radio network design.

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

 Course Policies : Attendance to Lab session is mandatory.

[ProtIOT] Iot Communication Protocols (KSENTINI, Adlen)
Adlen Ksentini

[ProtIOT] Iot Communication Protocols (KSENTINI, Adlen)

This course covers the Low Power Wide Area Network (LPWAN) protocols dedicated to IOT. LPWAN is a technology that intends to offer Internet connectivity to a large number of objects   ("Things") under very strict requirements in terms of cost, power consumption, long distance, battery life, indoor penetration, etc. This course presents two families of LPWA protocols specially developed for IOT: (i) LPWAN for unlicensed spectrum (e.g. LoRa, SigFox ...) and (ii) Cellular LPWAN for licensed spectrum (e.g. 3GPP LTE Cat. M1 or Cat. NB).

Teaching and Learning Methods: The course is organized in 4 lectures and 3 labs.

Course Policies: Labs are Mandatory (attendance + reports)

[PlanTP] Transportation Planning (HÄRRI, Jérôme)
Jerome Haerri

[PlanTP] Transportation Planning (HÄRRI, Jérôme)

(Course for Post Master  et  international Master students only).This module addresses mechanisms and strategies to model multi-modal transportation. The objectives are first to introduce concepts of population modeling. Second, it extends graph theory concepts for modeling heterogeneous transportation networks, in particular public transport networks. Mechanisms for activity and demand modeling for multimodal transportation will also be discussed. Finally, this lecture trains on best practices to apply these concepts for efficient multimodal transportation planning on vehicular traffic simulator

 

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

Course Policies :  Attendance to Lab session is mandatory.

[NetSoft] Network Softwerization (KSENTINI, Adlen)
Adlen Ksentini

[NetSoft] Network Softwerization (KSENTINI, Adlen)

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 Computing (mainly 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, e.g. Anything as a Service (AaaS) and Network Slicing. 

Teaching and Learning methods:

-          Be able to control a network using a NoS (SDN controller)

-          Be able to deploy a virtual network architecture

[Net_Sec] Network Security: practical hands on approach" (DACIER, Marc)
Marc Dacier

[Net_Sec] Network Security: practical hands on approach" (DACIER, Marc)

This course presents the main applications of secure communication mechanisms in the area of computer networks and distributed systems. The course covers network security approaches based on firewalls, cryptographic security protocol suites designed for the data exchange and network control components of Internet, wireless security protocols, and security solutions for mobile network architectures.

Teaching and Learning Methods : Lectures and Lab sessions

Course Policies : Attendance to Lab sessions is mandatory.

[MobWat] Wireless Access Technologies (HÄRRI, Jérôme)
Jerome Haerri

[MobWat] Wireless Access Technologies (HÄRRI, Jérôme)

This module addresses the access methods in Wireless Local Access Networks (WLAN). The basic contention and management mechanisms are detailed. Current and emerging standards of WLAN toward 5G are also presented.

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

Course Policies :  Attendance to Lab session is mandatory.

[MobAdv] Mobile Advanced Networks (NIKAEIN, Navid)
Navid Nikaein

[MobAdv] Mobile Advanced Networks (NIKAEIN, Navid)

Teaching and Learning Methods: Lectures, Homework (2-3), and a case study (study and present a research paper in a group of 2-3 students). 

Course Policies: Mandatory participation, Case study optional, but recommended.

[MALCOM] Machine Learning for Communication Systems (KOUNTOURIS, Marios)
Marios Kountouris

[MALCOM] Machine Learning for Communication Systems (KOUNTOURIS, Marios)


This course introduces fundamental concepts in machine learning (ML), with particular emphasis on communication-efficient distributed learning and applications to networked systems. After a brief introduction to ML methods and deep neural networks (adapted to enrolled students’ prior knowledge), we present key aspects for their efficient application to communication systems. We will cover applications that span different layers and system configurations, including physical layer (signaling, detection), multiple access and radio resource management. We then focus on large-scale distributed and decentralized learning in wireless networks, in particular under constraints (completion time, radio resources, computational efficiency, etc.). We also cover reinforcement learning and theoretical ML topics (generalization, approximation, fairness). Finally, we highlight key challenges in realizing the promise of machine learning for communication networks.

Teaching and Learning Methods: Lectures, exercise sessions, lab sessions, and potentially homework assignments including both problem solving and programming of learned methods. Each session starts summarizing key concepts from previous lecture. Part of each lecture is dedicated to illustrative examples and exercises.

Course Policies: Attendance to lab session is mandatory. Attendance to lectures and exercise sessions is highly recommended.


[ImSecu] Imaging Security (DUGELAY, Jean-luc)
Jean-Luc Dugelay

[ImSecu] Imaging Security (DUGELAY, Jean-luc)


Image & Video processing is part of many applications related to security: digital watermarking, steganography, image forensics, biometrics, and video surveillance.

  • Digital Watermarking allows owners or providers to hide an invisible and robust message inside a digital Multimedia document, mainly for security purposes such as owner or content authentication. There is a complex trade-off between the different parameters : capacity, visibility and robustness.
  • Steganographyis the art and science of writing hidden messages (in a picture or a video) in such a way that no-one apart from the sender and intended recipient even realizes there is a hidden message.
  • Image Forensics includes two main objectives: (1) To determine through which data acquisition device a given image is generated; (2) To determine whether a given image has undergone any form of modification or processing.
  • Biometrics: The security fields uses three different types of authentication : something you know, something you have, ore something you are : a biometric. Common physical biometrics includes fingerprints, hand geometry ; and retina, iris or facial characteristics. Behavioural characters include signature, voice. Ultimately, the technologies could find their strongest role as intertwined and complementary pieces of a multifactor authentication system. In the future biometrics is seen playing a key role in enhancing security, residing in smart cards and supporting personalized Web e-commerce services. Personalization through person authentication is also very appealing in the consumer product area. This course will focus on enabling technologies for Biometrics, with a particular emphasis on person verification and authentication based on or widely using image/video processing.
  • Video surveillance is the monitoring of the behavior, activities, or other changing information, usually of people for the purpose of influencing, managing, directing, or protecting. By default, for a better scene understanding, automatic image processing tools are used between acquisition/transmission and visualization or storage

Teaching and Learning Methods: Ce cours comporte un nombre limité de Travaux Pratiques et Travaux Dirigés.


 

Course Policies: Les TPs sont obligatoires.


[FormalMet] FormalMethods-Formal specification and verification of systems (AMEUR, Rabea)
Rabea Ameur

[FormalMet] FormalMethods-Formal specification and verification of systems (AMEUR, Rabea)

The aims of the course are to provide students with tools that can help to design error-free software/hardware systems. The course  gives both the theoretical foundations and the pratical use of formal methods.

Teaching and Learning Methods : Lectures and Lab sessions.

Course Policies : Attendance to Lab session is mandatory.

[Forensics] Cyber-crime and Computer Forensics (BALZAROTTI, Davide)
Davide Balzarotti

[Forensics] Cyber-crime and Computer Forensics (BALZAROTTI, Davide)

 

The course is roughly divided in two separate parts. The first covers the   topics of computer forensics and incident response. In particular, we   discuss a number of techniques and open source tools to acquire and   analyze network traces, hard disk images, Windows and Linux operating   system artifacts, log files, and memory images.

The second part of the course deals with the analysis of malware and   unknown binaries. Here the goal is to introduce students to the main  classes of techniques used in malware analysis and reverse engineering.   We cover both static techniques (ELF and PE file structures,   dissasseblers and decompilers, data and control flow analysis, abstract   interpretation, ...) and dynamic techniques (sandboxing, library and   syscall traces, dynamic instrumentation, debugging, taint analysis,   unpacking,...). We will use mostly open source tools, with the exception of IDA Pro.

Teaching and Learning Methods :  Lectures and Homework Assignment

[DigitalSystems] Digital systems, hardware - software integration (PACALET, Renaud)
Renaud Pacalet

[DigitalSystems] Digital systems, hardware - software integration (PACALET, Renaud)

This course provides an overview of software and hardware design for smart objects. It shows how to specify, design and validate digital hardware components, how to integrate them in a microprocessor-based system, and how to drive them from the software layers.

Teaching and Learning Methods: Lectures, team-work, lab sessions. Students are provided with prototyping boards and design tools for the whole semester duration.

Course Policies: Attendance to the lab sessions is mandatory

[DeepLearning] Deep Learning (MICHIARDI, Pietro)
Pietro Michiardi

[DeepLearning] Deep Learning (MICHIARDI, Pietro)


Deep Learning is a new approach in Machine Learning which allows to build models that have shown superior performance fora wide range of applications, in particular Computer Vision and Natural Language Processing. Thanks to the joint availability of large data corpus and affordable processing power, Deep Learning has revived the old field  of Artificial Neural Networks and provoked the "Renaissance" of AI (Artificial Intelligence). The objective of this course is to provide an overview of the field of Deep Learning, starting from simple Neural Network architectures and pursuing with contemporary and state of the art practices and models. The course is organized as a combination of  lectures where the theory is exposed and discussed, and hands-on sessions (labs) where experimentations are performed to practice with the theoretical concepts.

Teaching and Learning Methods : The course is composed of a combination of lectures and labs.

Course Policies : Attendance to all sessions is mandatory.


[CompMeth] Computational Methods for digital communications (KNOPP, Raymond)
Raymond Knopp

[CompMeth] Computational Methods for digital communications (KNOPP, Raymond)

Computational methods in digital communications provides a selection of hands-on experiments in programming and implementation techniques for high-performance computing applied to telecommunications. Students will learn architecture concepts and how to optimize software to implement different types of algorithms efficiently on software-based systems.

Teaching methods: The course is primarily given in a lab setting leveraging guided personal work. The instructor gives some lecture material progressively during the course of the lab-sessions in an on-demand fashion. The mini-project teamwork includes regular 45 minutes review meetings with the instructor and teaching assistants to overview progress on the subject.

Grading: 50% final mini-project work and presentation, 50% grading of lab sessions.

[ASI] Advanced Statistical Inference
Maurizio Filippone

[ASI] Advanced Statistical Inference


This course focuses on the principles of learning from data and quantification of uncertainty, by complementing and enriching the Introduction to Statistical Learning course. In particular, the course is divided into two main parts that correspond to the supervised and unsupervised learning paradigms. The presentation of the material follows a common thread based on the probabilistic data modeling approach, so that many classical algorithms, such as least squares and k-means, can be seen as special cases of inference problems for more general probabilistic models. Taking a probabilistic view also allows the course to derive inference algorithms for a class of nonparametric models that have close connections with neural networks and support vector machines. Similarly to the Introduction to Statistical Learning course, the focus is not on the algorithmic background of the methods, but rather on their mathematical and statistical foundations. This advanced course is complemented by lab sessions to guide students through the design and validation of the methods developed duringthe lectures.

Teaching and Learning Methods : Lectures and Lab sessions (preferably one student per group)

Course Policies : Attendance of Lab sessions is mandatory


[APPIOT] Iot Application Protocols (KSENTINI, Adlen)
Adlen Ksentini

[APPIOT] Iot Application Protocols (KSENTINI, Adlen)

This course covers the application-level protocols dedicated to IOT. Knowing the limited capacity, in terms of battery and CPU, of the things, the classical application protocols used in the Internet like HTTP are not adequate. This course presents the recent application protocols specially developed for IOT. These protocols are organized into two categories: (i) Client/server (like COAP) and (ii) Publish/Subscribe (like MQTT, XMPP). In addition to these protocols, this course introduces two types of architecture, specifically dedicated to host IOT services, like 3GPP MTC and oneM2M.

Teaching and Learning Methods: The course is organised in lectures and labs.

Course Policies: Labs are Mandatory (attendance + reports)

[AML] Algorithmic Machine Learning (MICHIARDI, Pietro)
Pietro Michiardi

[AML] Algorithmic Machine Learning (MICHIARDI, Pietro)

The goal of this course is to offer data science projects to students to gain hands-on experience with several aspects of machine learning in the wild. It nicely merges the theoretical concepts students can learn in our courses on machine learning and statistical inference, and systems concepts we teach in systems courses.

The prerequisites for this course are: MALIS and Deep Learning. Optionally, familiarity with the concepts developed in the ASI course can be useful too. Students who never took any course in machine learning may experience an overload, due to both the necessity to master theoretical aspects and computer science aspects of the field.

This course is organized around Jupyter notebooks. Notebooks require to address several challenges, that can be roughly classified in:

* Data preparation and cleaning
* Building descriptive statistics of the data
* Working on a selected algorithm, e.g., for building a statistical model
* Working on experimental validation

Students are expected to work throughout the semester on their projects, in groups. As this is a very practical course, there is no frontal lecture.

[3DGraph] 3-D and virtual imaging (analysis and synthesis) (GROS, Pascal)
Pascal Gros

[3DGraph] 3-D and virtual imaging (analysis and synthesis) (GROS, Pascal)

This course introduces the main concepts and techniques used in computer graphics and image synthesis. It focuses on 3D object modelling and advanced visualization methods used in 3D and Virtual imaging, scientific and information visualization, CAD, flight simulation, games, advertising and movie special effects. The courses mixes theoretical and practical sessions and the project requires a student personal involvement.

Teaching and Learning Methods  :Lectures, Lab sessions and project  (groups of 2 to 4 students)

Course Policies : It is mandatory to

  • Attend to all the Lab sessions
  • Deliver of a project movie and attend the project contest.

[HWSec] Hardware Security (PACALET, Renaud)
Renaud Pacalet

[HWSec] Hardware Security (PACALET, Renaud)

This course offers a survey of several well-known attacks targeting specific weaknesses of hardware (microprocessors, dedicated hardware cryptographic accelerators...) For each of them the conditions of success are explained and some countermeasures are proposed.

Teaching and Learning Methods : Lectures, lab sessions 

[TeamLead] Personal Development and Team Leadership (POPE, Kenneth)
Ken Pope

[TeamLead] Personal Development and Team Leadership (POPE, Kenneth)


This course consists of three essential elements:


(1)     'Know yourself' - understanding the drivers of your own behavior. This is the basis of any personal development and is critical for developing effective interaction with others whether as a team member, or as a team leader.


(2)    'Working with others' - building on the self-knowledge mentioned above, this core element allows you to explore, understand, and practice ways of working with others that are both more enjoyable and more effective. This is critical given that almost everyone works as part of a team.


(3)    'What's next?' - building on both the above sections, this element helps you take the next steps in your career: setting objectives, selecting target organizations, applying for jobs, and effective interviewing.

Teaching and Learning Methods: Lectures, team exercises, and presentations

Course Policies: On-time class attendance is mandatory; three unapproved absences mean exclusion.


[SATT] Sociological Approaches of Telecom Technologies (RELIEU, Marc)
Marc Relieu

[SATT] Sociological Approaches of Telecom Technologies (RELIEU, Marc)

Contemporary works in the sociology of Technology offer numerous critics of the classical divide between technical and social features. It has been shown that the success or failure of technical innovations rests on their propensity to merge with various organizational and interactional features. This course aims at providing students with a precise understanding of different combinations between technologies and conversational features. Various case studies of  technologies in use will be examined, either in professional or ordinary or in mundane contexts.  Drawing from those studies, the course provide several methodological  discussions, with a strong focus on observation of social conduct in natural settings and the use of audio or video recordings in social science.

Teaching and Learning Methods : Lectures , written and oral presentations and discussions, readings

Course Policies : On-time attendance is mandatory

[ProjMan] Project management (AUREGLIA, Jean Jacques)
Jean-Jacques Aureglia

[ProjMan] Project management (AUREGLIA, Jean Jacques)

The project oriented approach is considered in leading companies as an efficient method to manage both market and client oriented deliverables (i.e. products and services) as well as investments. In order to better manage and control projects, enterprises often evolve from a “Functional organisation” into a “Matrix organisation”, in which a new breed of leaders appears: Project Managers. The Project Management Profession becomes a key element in the new and global enterprise model.
The EURECOM Global Project Management class aims at introducing the different Project Management concepts and techniques, mixing “main tent” presentation of key topics and hands-on case studies for each student to experience team dynamics and managing sample projects.

Teaching and Learning Methods include Lectures (all attendees) and Case Study sessions (in groups).
A case study in the technology domain will be performed during the course, from session to session. The main purpose of this case study is to illustrate, use and get familiar with the different Project Management methods and techniques introduced during the lectures. The case study may require some work between the class sessions. Students will present their work to the whole class for the purpose of sharing and obtaining feed-back. In order to optimize the effectiveness of each session, the students will be expected to keep the topics addressed in earlier sessions fresh in their mind, prepare for the case study, and actively participate through questions and presentation of the results of the case study.

Course Policies: Attendance is Mandatory for Both Lectures and Case Study sessions.
Non-attendance would need to be justified by serious reasons and limited to a maximum of 2.  
Active participation in the Case Study sessions is expected.

[Business] Business Simulation (POPE, Kenneth)
Ken Pope

[Business] Business Simulation (POPE, Kenneth)


In the Business Simulation course, students, in groups of four to six, will manage a virtual company as an aid to learning, by doing, about the practical aspects of running a company in a dynamic international environment. The course will be provided in a compact blended learning environment.

Teaching and Learning Methods:


Uniquely at Eurecom, this course will be delivered in a blended learning environment. That is, only half of the learning will take place in the classroom at fixed times each week. The other half of the course will be undertaken online at times, and places, suitable for the individual student teams, provided that the required tasks (usually a decision set) is completed within the defined week timeframe.


Research has shown that the best learning experience from the business simulation is over a concentrated timeframe. Therefore, this course, of the standard 42 hours effective learning time for a 5-credit program, will be completed over seven weeks elapsed time (rather than the standard 14 weeks). Some students may find this helpful; freeing up time towards the end of the semester to work on projects in other courses.


During the course, following initial briefings, student teams will each take up to 12 sets of business decisions; each decision set representing one quarter of a business year. Decisions are entered online before a predefined cutoff date and time. These decision sets drive the simulation, the results being provided online. During the seven classroom sessions, instructors will be available, face-to-face, to answer questions and provide support. Between the classroom sessions, instructors are available online (asynchronously, and, at pre-agreed times, live), as are a range of online support materials, including videos and guides.


Teaching will combine classroom and video-based instruction, guidance, and support, with additional online materials and individual support helping students, at their own time and pace, to master the technical and practical aspects of the simulation.

Course Policies


Active participation is required from each student. The grading system is continuous (see below) and is on both team and individual results. On-time attendance at entire classroom sessions is mandatory and will be recorded. Unapproved absences may result in expulsion. Individual student participation during the online sessions will be monitored by the teams themselves. Peer reviews are part of the evaluation process. In the final classroom session, each team, involving each individual student, will present their results to the class and to assessors.