MOODLE EURECOM

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 provides an overview of the architecture of microprocessor-based systems. It presents the main hardware and software components and the concept of instruction set which can be considered as the hardware-software interface. It is intended for students who never had courses in computer architecture.

Teaching and learning methods:

• Understanding of the structure of a computer system, of its main hardware and software components of their role
• Programming in assembly language
• Understand how hardware and software interact

Course  Policies: /

Programming Languages and Algorithm Design has the following objectives:

• 
  Introduce students to basic programming concepts and programming tools;
• 
  Study three programming languages: C, as an example of low-level language; Python, as an example of high-level language; and MATLAB, as an example of domain-specific language;
• 
  Provide students with methods and knowledge suitable for the design of efficient algorithms;
• 
  Provide methods for the analysis of resources (memory and time) used by algorithms;
• 
  Provide a catalogue of the most well-known and efficient algorithms for basic computational problems (sorting, searching, resource optimization, etc.).

Teachind and Learning methods: 

The course is composed by lectures and laboratory sessions. The laboratory sessions consist in interactive crash courses (2 sessions of 3 hours each for each programming language, see “Course Structure” below) where the students have the opportunity of learning the basic programming concepts of different programming languages.

Course policies:

The course is composed by lectures, laboratory sessions in the form of interactive crash courses, and a mini student project.

Interactive:

The laboratory sessions will be interactive and will include discussions with the students and lab exercises. It will be held in one lab room equipped with a projector.

Crash curse:

The course will be structured into two sessions of 3 hours each.

The first session will serve as introduction to the programming language and corresponding programming tools. After a short description of the purpose and characteristics of the environment and programming language, the students will be guided through lab exercises to familiarize with the software and the basic programming concepts.

The second session will introduce additional libraries with a particular emphasis on the ones used in EURECOM courses.

The discussion will be alternated with lab exercises. The students will be given some time to solve a problem and then a volunteer student will be asked to show their solution to the other students using the computer connected to the projector.

The objective is to ensure that all students participate in the discussions and present their solution at least once during the course.

This course covers the implementation of database systems by addressing the main topics, including data storage, indexing, querying; query optimization and execution; concurrency control and transaction management.

The purpose of the course is to become familiar with the principles and the ideas behind established techniques for handling data at scale. Students will implement classic and cutting-edge database systems methods in three projects. Projects represent the biggest chunk of this course. The projects require extending the functionality of a data management system in order to support novel features. In at least one the projects, students will also write a technical report that describes and experimentally evaluates the built system.

The 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 : 

Students are expected to do their own assigned work. If it is determined that a student has engaged in any form of academic dishonesty, he or she may fail the course and additional sanctions according to Eurecom's policies.

This course presents the architecture of microprocessor-based systems, from the internals of the processors themselves to the main peripherals that surround them and make a complete computing machine, capable of running operating systems like GNU/Linux, Android, Windows, iOS...

Teaching and Learning Methods : Lectures, team-work, lab sessions, mini-conferences by industrials 

Course Policies : Attendance to the lab sessions and the mini-conferences is mandatory

This class approaches wireless communication from the perspective of digital signal processing (DSP). No background in digital communication is assumed, though it would be helpful. The utility of a DSP approach is due to the following fact: wireless systems are bandlimited. This means that with a high enough sampling rate, thanks to Nyquist’s theorem, we can represent the bandlimited continuous-time wireless channel from its samples. This allows us to treat the transmitted signal as a discrete-time sequence, the channel as a discrete-time linear time invariant system, and the received signal as a discrete-time sequence. In this class, we take an experimental approach to wireless digital communication. We will use a well-known software defined radio platform known as the USRP (universal software radio peripheral) where the radio can be programmed in software instead of implemented using hardware. The focus will be on the design, implementation, evaluation, and iterative optimization of a digital wireless communication link. At the end of this class, you will have constructed your own wireless communication link. In this process, you will have achieved the following learning objectives. You will understand what bandlimited system is and how sampling works. You will be able to compute power spectra of bandlimited signals. You should be able to describe the design challenges associated with building a wireless digital communication link. You should be able to define and calculate bit error rates for some common modulation schemes. You should know the difference between binary phase shift keying and quadrature phase shift keying as well as how to implement them. You should understand the connection between pulse shaping and sampling. You should know how to define excess bandwidth for a raised-cosine pulse. You should understand how to obtain a sampled channel impulse response from a continuous time propagation channel. You should understand how to train and estimate the coefficients of a frequency selective channel. You should understand the various kinds of synchronization required and how to compensate for different sources of asynchronicity. You should be able to explain how to perform equalization using single carrier frequency domain equalization or OFDM modulation.

Teaching and Learning methods: /

Course Policies:/

The goal of this course is to provide a comprehensive view on recent topics and trends in distributed systems and cloud computing. We will discuss the software techniques employed to construct and program reliable, highly-scalable systems. We will also cover architecture design of modern datacenters and virtualization techniques that constitute a central topic of the cloud computing paradigm. The course is complemented by a number of lab sessions to get hands-on experience with Hadoop and the design of scalable algorithms with MapReduce.

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

Course Policies: Attendance to Lab session is mandatory.

The goal of this course is to equip students with security and privacy technologies for the Big Data and the cloud computing paradigm. Students will discover the latest advances in privacy and security technologies and will understand their limitations as well.

Teaching and Learning Methods : Lectures (sometimes invited) and homeworks

Course Policies : homeworks and final project are mandatory

« 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

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).

 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.

Interested in learning how to communicate using quantum bits? Curious about how quantum algorithms and quantum computers work? This is an introductory course to quantum communication, computation, and information processing. We will cover various aspects of quantum information science and systems, introducing in a simple manner key principles and concepts, which are often considered “hard” or mysterious.

After a brief overview of quantum technology, the course starts with a concise introduction of key principles of quantum mechanics. Then, we cover fundamental aspects of quantum information, such as qubit, entanglement, Bell inequalities, and EPR (Einstein-Podolsky-Rosen) paradox, as well as of quantum communication (noise, quantum channels, decoherence, von Neumann entropy, Holevo capacity). We also present basic principles of quantum computing and study key quantum algorithms (e.g., Shor’s, Grover’s, quantum Fourier transform). Finally, we discuss potential applications and emerging topics, such as quantum AI and quantum/ post-quantum security.

Previous exposure to quantum mechanics is not required. All necessary concepts and mathematical formalism are taught during the first lectures.

Teaching and Learning Methods: Lectures supported by illustrative examples and exercises. Each session starts summarizing key concepts from previous lecture. Optional project for in-depth study of theoretical concepts or for understanding practical aspects (e.g. implementing/programming basic quantum algorithms and gates).

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

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

Optimization theory (convex, non-convex, discrete) is an important field that has application to almost every technical and non-technical field, including wireless communication, networking, machine learning, security, transportation systems, finance (portfolio management) and operation research (supply chain, inventory). It has recently attracted even more interest, as the underlying tool for advanced machine learning techniques for big data (deep neural networks, stochastic gradient descent). In this course we will cover both the fundamentals (algorithms, duality, convergence, optimality) as well as a range of applications in the area of (network) resource allocation, distributed optimization, and machine learning. Special emphasis will be devoted to exemplify applications of optimization techniques to modern engineering and CS problems with the objective of  developing skills and background necessary to recognize, formulate, and solve optimization problems.

Teaching and Learning Methods : Lectures supported by exercise sessions, and homework assignments including both problem solving and programming of learned methods (CVX, matlab, python)

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

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 presents the three main mobile platforms and their ecosystems, namely Android, iOS, and PhoneGap/WebOS. It explores emerging technologies and tools used to design and implement feature-rich mobile applications for smartphones and tablets taking into account both the technical constraints relative to storage capacity, processing capacity, display screen, communication interfaces, and the user interface, context and profile. 

 Teaching and Learning Methods : Lectures, Lab sessions (group of 2 students), and a challenge project ( group of up 2 4 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.

The objective of this course is to give students a solid background in Machine Learning (ML) techniques. ML techniques are used to build efficient models for problems for which an optimal solution is unknown. This course will introduce the basic theories of Machine Learning, together with the most common families of classifiers and predictors. It will identify the basic ideas underlying the mechanism of learning, and will specify the practical problems that are encountered when applying these techniques, optimization, overfitting, validation, together with possible solutions to manage those difficulties.

Teaching and Learning Methods : Lectures and Lab sessions (groups of 1 or 2 students)

Course Policies : Attendance to Lab sessions is mandatory

The course aims at providing students with a basic knowledge and practice about the use of computer algorithms to perform image processing on digital images. The two main objectives attached to Digital Image Processing (DIP) are to improve the visual quality of images and to automatically extract semantic information from visual data (e.g. object recognition). 

Teaching and Learning Methods: Each session is split into two parts: 1.5-hour lecture and 1.5-hour lab.

 Course Policies:  It is mandatory to attend lab. sessions.

Because multimedia data (in particular image and video) require efficient compression techniques in order to be stored and delivered, image and video compression is a crucial element of an effective communication system.

This course covers the most popular lossless and lossy formats, introduces the key techniques used in source coding, as well as appropriate objective/subjective metrics for visual quality evaluation.

Teaching and Learning Methods: Each class includes a problem session for students to practice the material learned. This course includes a limited number of lab session hours.

Course Policies: It is mandatory to attend lab. sessions.

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

This module teaches the fundamentals of simulation and emulation methodologies providing guidance on how to design a performance evaluation campaign, set up a test scenario, select the appropriate models, level of granularity, metrics for statistical correctness, and discuss the differences between simulation and emulation platforms and how to use them for accurate performance evaluation of communications for ITS.

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

Course Policies :  Attendance to Lab session is mandatory.

Digital communications is the study of physical layer transmission and reception strategies in communications equipment such as mobile communication devices, high-speed ethernet, optical communications and subscriber-line communications. It comprises the areas of (i) statistical modelling of communication channels (ii) design of coding and modulation systems for error resiliency (iii) design of demodulation and decoding strategies and the associated methods for assessing their performance. In addition to providing an overview of communication channels and classical modulation strategies, this course takes a hands-on approach by focusing on the details of a few critical elements of digital communications pertaining to modern wireless transceivers, both coherent and non-coherent.

Teaching and Learning Methods :: Lectures and hands-on lab sessions in MATLAB using signals acquired from local 4G networks.

Course Policies : Attendance to lab sessions is mandatory.

Hot Topics in Mobile Communications

·         This course presents some recent or emerging  HOT TOPICS within the area of mobile networks.

·         The course is modified from 2014 (and earlier) versions to allow focus on updated set of hot topics and trends in mobile communications.

·         We emphasize emerging techniques to be used in future 5G mobile networks to allow for a significant increase in user quality, and network capacity.

·         The course earns 5 ECTS

·         We cover hot topics for 5G such as "Massive MIMO" , "network cooperation", "interference management", and "device coordination". These topics cover 21 hours.

·         In the other 21hours, external experts (Intel, Huawei, ETSI, etc.) from Industry reveal hot topics seen from the wireless networking  industry.

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

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

This course is an introduction to responsible innovation (RI), notably in the digital domain. It provides conceptual and empirical bases for students to approach technology and innovation by focusing on the issues they raise in terms of ethics and risks for human health and the environment. These issues will be addressed from a sociological perspective based on a multilevel analysis of stakeholders' action. Students will learn the key concepts of the RI field (eg sustainability, acceptability, anticipation, participation) as well as some technology assessment methods, that will be useful to better understand the contemporary challenges and controversies of the digital era. The seven sessions of the course will be structured around a permanent dialogue between researchers and practitioners of innovation. They will will cover a range of case studies including human exposure to EMF, smart meters for energy transition, e-waste and digital identity.

Teaching and Learning Methods: The course is organized as a workshop where the sessions will be held by social scientists and innovation practitioners from outside the academic field (industry, consulting). It is based on a participative pedagogy, requiring the active involvement of students, both in class (discussions, presentations, tutorials, MCQ) and at home (readings, web research, writing). A collective research work of knowledge formalization will be required throughout the course and presented at the end of the course.

Course Policies: On-time class attendance is mandatory and will be recorded at each session. Each not excused absence will reduce the final grade by three points. If you are more than 15 minutes late, you will be counted absent. After three absences, even if excused, you will have to pass a final exam. 

This course provides a solid introduction to intellectual property law from a managerial and strategic perspective taking an international and comparative approach.

The course is the study of how companies protect innovations   in order to create value for the company

Teaching and Learning Methods : Lectures and cases  sessions (group of 4 students)

Course Policies : Attendance to Lab session is mandatory.

Course for EIT students only

Two topics – with related concepts, methods and/or tools – will be covered in the context of a selected innovation or entrepreneurial case:

•             One fixed and common topic: Assessing the impact of a technology on an industry, market and/or organization, the support and barriers to its deployment, the influence on a specific goal/agenda (technology transfer, existing industry, new company, etc.).

•             One case-dependent topic: pertaining to market / business environment analysis (main forces affecting the business, suppliers, partners, competition, environmental issues), sustainability and social issues, business modeling, go-to-market strategies, etc.

The innovation or entrepreneurial project may be originating from:

•             Cases issued from EIT Digital Innovation Action Lines: within Activities, Partners / Business Community projects,

•             Cases based on the continuation of students EIT Digital Summer School (or BDLab) project,

•             Cases within other innovation or entrepreneurial projects rooted in a real-life environment as may be collected in the university ecosystem.

Teaching and Learning Methods :

The I&E Study is based on a group assignment and on an individual assignment. A large autonomy is given to the students to organize and achieve their goals.

a. Group assignment:

Students will work in teams of around 4 students. Teams will be assigned cases. Each team will identify and address a challenge/question in the context of their assigned case. This challenge/question may be related to considering alternate business models or go-to-market scenarios in relation with the innovation or entrepreneurial case, fed by exploration in some specific areas: business environment, competition, suppliers, partners, environmental and sustainability issues, etc.

To address their challenge/question, the students will cover the 4 generic steps of an explorative business analysis:

• Identification of the relevant challenge/question,
• Acquisition of applicable concepts/methods/tools,
• Observations (data collection) on a selected part of the case,
• Analysis and interpretation.

Students’ supervision comprises:

• One individual or group session to introduce and detail the assignment and help students identify the challenges/questions,
• 1-to-many workshops: one for each of the 4 study steps above. Students support can be structured through pre-/post-assignments for each workshop,
• One oral defense.

It is expected that the authors of the case will contribute to supervision, provide data/contacts, participate to an oral defense and/or offer other meeting occasions to ensure rooting of the business assignment in real life.

b. Individual assignment:

Students will work on an online individual assignment. This individual assignment can be done before, during or after the group assignment. In this assignment, students will acquire concepts and tools pertaining to the assessment of the impact of a technology on an industry, market and/or organization. Students will also get the opportunity to apply these concepts and tools to their own case (based on their group-assignment case when possible).

Students’ supervision comprises (may be combined with the group assignment supervision):

• One group session to introduce and detail the assignment,
• 1-to-many workshops to help students follow the steps in the assignment structure.

Cour

Thiscourse aims to start with the circular flow of economic activity and the interdependence of its institutional sectors. The evolution of sustainable development’s concept is developed with its economic, social and environmental dimensions. It highlights the challenges and commitments to be met at national and international level. Corporate social responsibility towards the various stakeholders is an essential step in the achievement of a sustainable economy.

Teaching and Learning Methods

A course of 5 sessions around the fundamentals of the concept of sustainable development.

Students will then have to apply their knowledge in 2 sessions through:

• Presentation (student subgroups) on a topical subject related to sustainable economy challenges.
• Debriefing and discussion

Course Policies: Seven 3-hour sessions.

Thanks to this class, you will understand why an innovative project cannot be considered as a regular project.You will learn how to take the market into account at a very early stage in project management, how to link technology and needs and how to lead in a time of uncertainty and complexity. You will be trained to build a solid and convincing launching strategy as a team on specific cases that you choose. Whatever the nature or maturity of a project, you will learn to identify its potential and market, to choose the most relevant positioning and to build a viable business model.

You have an innovative idea that you would like to expand in the market. You want to know if you are made for entrepreneurship. You plan to work in a R&D company or in an innovation department. Or you are simply curious to know how to innovate better. This course is for you!

Teaching and learning methods:

The pedagogy of this course is deliberately interactive through the application of new concepts studied on concrete cases. We will use the pedagogical tools developed by Vianeo (card game, paper canvas, digital platform) and the participants' cases throughout the course in teams of 5 or 6.

Course Policies: Mandatory attendance. Between 2 courses, work on collecting information, further analysis, updating or preparation.

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.

The goal of this course is to equip students with security and privacy technologies for the Big Data and the cloud computing paradigm. Students will discover the latest advances in privacy and security technologies and will understand their limitations as well.

Teaching and Learning Methods : Lectures (sometimes invited) and homeworks

Course Policies : homeworks and final project are mandatory

Digital communications is the study of physical layer transmission and reception strategies in communications equipment such as mobile communication devices, high-speed ethernet, optical communications and subscriber-line communications. It comprises the areas of (i) statistical modelling of communication channels (ii) design of coding and modulation systems for error resiliency (iii) design of demodulation and decoding strategies and the associated methods for assessing their performance. In addition to providing an overview of communication channels and classical modulation strategies, this course takes a hands-on approach by focusing on the details of a few critical elements of digital communications pertaining to modern wireless transceivers, both coherent and non-coherent.

Teaching and Learning Methods :: Lectures and hands-on lab sessions in MATLAB using signals acquired from local 4G networks.

Course Policies : Attendance to lab sessions is mandatory.

This course presents the architecture of microprocessor-based systems, from the internals of the processors themselves to the main peripherals that surround them and make a complete computing machine, capable of running operating systems like GNU/Linux, Android, Windows, iOS...

Teaching and Learning Methods : Lectures, team-work, lab sessions, mini-conferences by industrials 

Course Policies : Attendance to the lab sessions and the mini-conferences is mandatory

The goal of this course is to provide a comprehensive view on recent topics and trends in distributed systems and cloud computing. We will discuss the software techniques employed to construct and program reliable, highly-scalable systems. We will also cover architecture design of modern datacenters and virtualization techniques that constitute a central topic of the cloud computing paradigm. The course is complemented by a number of lab sessions to get hands-on experience with Hadoop and the design of scalable algorithms with MapReduce.

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

Course Policies: Attendance to Lab session is mandatory.

Wireless communications are pervasive and have been used for a century. They are used in a very large set of  security applications (communications by security forces, car key remote, alarm system, access control, drone command and control, surveillance devices) . However, day to day applications also require to be protected for privacy and personal security, such as WiFi or mobile communications (2G/3G/4G). At the same a number of challenges are present in wireless communications security, for example, messages are broadcasted, making it possible to intercept them without being noticed. Wireless signals are subject to jamming, making them unavailable.

This course will give a large perspective of the fundamental challenges in securing wireless communications, from the physical layer, modulations to the application protocols. A special focus will be put on practice with hands on exercises (using software defined radios and WiFi dongles).

Teaching and Learning Methods : Course is composed of lectures, Labs and small projects with final presentation.

Course Policies : Class attendance, labs and projects mandatory.

« 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

Statistics is a foundation of many areas of science and engineering that involve `` data.’’ This course focuses on fundamental concepts in statistical inference that are necessary for applying statistical methods in practice and that form the basis of other fields such as machine learning.

Teaching and Learning Methods:  Students learn by lectures, exercises, and computer experiments.

Course Policies:  The valid usage of statistical methods requires a mathematical understanding of the underlying mechanism. As such, the course covers both mathematical and algorithmic aspects of statistics.  

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).

 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.

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

Optimization theory (convex, non-convex, discrete) is an important field that has application to almost every technical and non-technical field, including wireless communication, networking, machine learning, security, transportation systems, finance (portfolio management) and operation research (supply chain, inventory). It has recently attracted even more interest, as the underlying tool for advanced machine learning techniques for big data (deep neural networks, stochastic gradient descent). In this course we will cover both the fundamentals (algorithms, duality, convergence, optimality) as well as a range of applications in the area of (network) resource allocation, distributed optimization, and machine learning. Special emphasis will be devoted to exemplify applications of optimization techniques to modern engineering and CS problems with the objective of  developing skills and background necessary to recognize, formulate, and solve optimization problems.

Teaching and Learning Methods : Lectures supported by exercise sessions, and homework assignments including both problem solving and programming of learned methods (CVX, matlab, python)

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

The goal of this course is to teach student how to model, analyze, and optimize the performance of different Networks using simple theoretical tools. The end goal is to highlight the common underlying properties, develop a strong high-level insight on the network parameters affecting network performance, and understand how to optimize a networked system. 

Each class will be a mix of some necessary theoretical tools, and their application to real-world networks. We will consider examples from modern cellular networks (e.g., offloading and load balancing), capacity planning, MAC protocols, scheduling in computing clouds and web server farms, security (e.g. virus infections), measuring large social networks like Facebook and Twitter, search engines (e.g. Google's PageRank algorithm), and many others. 

Teaching and Learning Methods :Lectures, Homework, and Programming Labs (2)

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 presents the three main mobile platforms and their ecosystems, namely Android, iOS, and PhoneGap/WebOS. It explores emerging technologies and tools used to design and implement feature-rich mobile applications for smartphones and tablets taking into account both the technical constraints relative to storage capacity, processing capacity, display screen, communication interfaces, and the user interface, context and profile. 

 Teaching and Learning Methods : Lectures, Lab sessions (group of 2 students), and a challenge project ( group of up 2 4 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.

The objective of this course is to give students a solid background in Machine Learning (ML) techniques. ML techniques are used to build efficient models for problems for which an optimal solution is unknown. This course will introduce the basic theories of Machine Learning, together with the most common families of classifiers and predictors. It will identify the basic ideas underlying the mechanism of learning, and will specify the practical problems that are encountered when applying these techniques, optimization, overfitting, validation, together with possible solutions to manage those difficulties.

Teaching and Learning Methods : Lectures and Lab sessions (groups of 1 or 2 students)

Course Policies : Attendance to Lab sessions is mandatory

Hot Topics in Mobile Communications

·         This course presents some recent or emerging  HOT TOPICS within the area of mobile networks.

·         The course is modified from 2014 (and earlier) versions to allow focus on updated set of hot topics and trends in mobile communications.

·         We emphasize emerging techniques to be used in future 5G mobile networks to allow for a significant increase in user quality, and network capacity.

·         The course earns 5 ECTS

·         We cover hot topics for 5G such as "Massive MIMO" , "network cooperation", "interference management", and "device coordination". These topics cover 21 hours.

·         In the other 21hours, external experts (Intel, Huawei, ETSI, etc.) from Industry reveal hot topics seen from the wireless networking  industry.

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

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

Interested in learning how to communicate using quantum bits? Curious about how quantum algorithms and quantum computers work? This is an introductory course to quantum communication, computation, and information processing. We will cover various aspects of quantum information science and systems, introducing in a simple manner key principles and concepts, which are often considered “hard” or mysterious.

After a brief overview of quantum technology, the course starts with a concise introduction of key principles of quantum mechanics. Then, we cover fundamental aspects of quantum information, such as qubit, entanglement, Bell inequalities, and EPR (Einstein-Podolsky-Rosen) paradox, as well as of quantum communication (noise, quantum channels, decoherence, von Neumann entropy, Holevo capacity). We also present basic principles of quantum computing and study key quantum algorithms (e.g., Shor’s, Grover’s, quantum Fourier transform). Finally, we discuss potential applications and emerging topics, such as quantum AI and quantum/ post-quantum security.

Previous exposure to quantum mechanics is not required. All necessary concepts and mathematical formalism are taught during the first lectures.

Teaching and Learning Methods: Lectures supported by illustrative examples and exercises. Each session starts summarizing key concepts from previous lecture. Optional project for in-depth study of theoretical concepts or for understanding practical aspects (e.g. implementing/programming basic quantum algorithms and gates).

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

The goal of this course is to introduce the students to the basic concepts of secure multiparty computation, the foundational MPC protocols and more advanced blockchains protocols.

Teaching and Learning Methods:

Lectures and homework.

Course Policies:

Final project and homework are mandatory

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

This module teaches the fundamentals of simulation and emulation methodologies providing guidance on how to design a performance evaluation campaign, set up a test scenario, select the appropriate models, level of granularity, metrics for statistical correctness, and discuss the differences between simulation and emulation platforms and how to use them for accurate performance evaluation of communications for ITS.

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

Course Policies :  Attendance to Lab session is mandatory.

This course is an introduction to responsible innovation (RI), notably in the digital domain. It provides conceptual and empirical bases for students to approach technology and innovation by focusing on the issues they raise in terms of ethics and risks for human health and the environment. These issues will be addressed from a sociological perspective based on a multilevel analysis of stakeholders' action. Students will learn the key concepts of the RI field (eg sustainability, acceptability, anticipation, participation) as well as some technology assessment methods, that will be useful to better understand the contemporary challenges and controversies of the digital era. The seven sessions of the course will be structured around a permanent dialogue between researchers and practitioners of innovation. They will will cover a range of case studies including human exposure to EMF, smart meters for energy transition, e-waste and digital identity.

Teaching and Learning Methods: The course is organized as a workshop where the sessions will be held by social scientists and innovation practitioners from outside the academic field (industry, consulting). It is based on a participative pedagogy, requiring the active involvement of students, both in class (discussions, presentations, tutorials, MCQ) and at home (readings, web research, writing). A collective research work of knowledge formalization will be required throughout the course and presented at the end of the course.

Course Policies: On-time class attendance is mandatory and will be recorded at each session. Each not excused absence will reduce the final grade by three points. If you are more than 15 minutes late, you will be counted absent. After three absences, even if excused, you will have to pass a final exam. 

Thiscourse aims to start with the circular flow of economic activity and the interdependence of its institutional sectors. The evolution of sustainable development’s concept is developed with its economic, social and environmental dimensions. It highlights the challenges and commitments to be met at national and international level. Corporate social responsibility towards the various stakeholders is an essential step in the achievement of a sustainable economy.

Teaching and Learning Methods

A course of 5 sessions around the fundamentals of the concept of sustainable development.

Students will then have to apply their knowledge in 2 sessions through:

• Presentation (student subgroups) on a topical subject related to sustainable economy challenges.
• Debriefing and discussion

Course Policies: Seven 3-hour sessions.

Course for EIT students only

Two topics – with related concepts, methods and/or tools – will be covered in the context of a selected innovation or entrepreneurial case:

•             One fixed and common topic: Assessing the impact of a technology on an industry, market and/or organization, the support and barriers to its deployment, the influence on a specific goal/agenda (technology transfer, existing industry, new company, etc.).

•             One case-dependent topic: pertaining to market / business environment analysis (main forces affecting the business, suppliers, partners, competition, environmental issues), sustainability and social issues, business modeling, go-to-market strategies, etc.

The innovation or entrepreneurial project may be originating from:

•             Cases issued from EIT Digital Innovation Action Lines: within Activities, Partners / Business Community projects,

•             Cases based on the continuation of students EIT Digital Summer School (or BDLab) project,

•             Cases within other innovation or entrepreneurial projects rooted in a real-life environment as may be collected in the university ecosystem.

Teaching and Learning Methods :

The I&E Study is based on a group assignment and on an individual assignment. A large autonomy is given to the students to organize and achieve their goals.

a. Group assignment:

Students will work in teams of around 4 students. Teams will be assigned cases. Each team will identify and address a challenge/question in the context of their assigned case. This challenge/question may be related to considering alternate business models or go-to-market scenarios in relation with the innovation or entrepreneurial case, fed by exploration in some specific areas: business environment, competition, suppliers, partners, environmental and sustainability issues, etc.

To address their challenge/question, the students will cover the 4 generic steps of an explorative business analysis:

• Identification of the relevant challenge/question,
• Acquisition of applicable concepts/methods/tools,
• Observations (data collection) on a selected part of the case,
• Analysis and interpretation.

Students’ supervision comprises:

• One individual or group session to introduce and detail the assignment and help students identify the challenges/questions,
• 1-to-many workshops: one for each of the 4 study steps above. Students support can be structured through pre-/post-assignments for each workshop,
• One oral defense.

It is expected that the authors of the case will contribute to supervision, provide data/contacts, participate to an oral defense and/or offer other meeting occasions to ensure rooting of the business assignment in real life.

b. Individual assignment:

Students will work on an online individual assignment. This individual assignment can be done before, during or after the group assignment. In this assignment, students will acquire concepts and tools pertaining to the assessment of the impact of a technology on an industry, market and/or organization. Students will also get the opportunity to apply these concepts and tools to their own case (based on their group-assignment case when possible).

Students’ supervision comprises (may be combined with the group assignment supervision):

• One group session to introduce and detail the assignment,
• 1-to-many workshops to help students follow the steps in the assignment structure.

Cour

Thanks to this class, you will understand why an innovative project cannot be considered as a regular project.You will learn how to take the market into account at a very early stage in project management, how to link technology and needs and how to lead in a time of uncertainty and complexity. You will be trained to build a solid and convincing launching strategy as a team on specific cases that you choose. Whatever the nature or maturity of a project, you will learn to identify its potential and market, to choose the most relevant positioning and to build a viable business model.

You have an innovative idea that you would like to expand in the market. You want to know if you are made for entrepreneurship. You plan to work in a R&D company or in an innovation department. Or you are simply curious to know how to innovate better. This course is for you!

Teaching and learning methods:

The pedagogy of this course is deliberately interactive through the application of new concepts studied on concrete cases. We will use the pedagogical tools developed by Vianeo (card game, paper canvas, digital platform) and the participants' cases throughout the course in teams of 5 or 6.

Course Policies: Mandatory attendance. Between 2 courses, work on collecting information, further analysis, updating or preparation.

This course provides a solid introduction to intellectual property law from a managerial and strategic perspective taking an international and comparative approach.

The course is the study of how companies protect innovations   in order to create value for the company

Teaching and Learning Methods : Lectures and cases  sessions (group of 4 students)

Course Policies : Attendance to Lab session is mandatory.

Wireless communications are pervasive and have been used for a century. They are used in a very large set of  security applications (communications by security forces, car key remote, alarm system, access control, drone command and control, surveillance devices) . However, day to day applications also require to be protected for privacy and personal security, such as WiFi or mobile communications (2G/3G/4G). At the same a number of challenges are present in wireless communications security, for example, messages are broadcasted, making it possible to intercept them without being noticed. Wireless signals are subject to jamming, making them unavailable.

This course will give a large perspective of the fundamental challenges in securing wireless communications, from the physical layer, modulations to the application protocols. A special focus will be put on practice with hands on exercises (using software defined radios and WiFi dongles).

Teaching and Learning Methods : Course is composed of lectures, Labs and small projects with final presentation.

Course Policies : Class attendance, labs and projects mandatory.

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

Course Policies : Attendance to Lab session is mandatory

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.

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.

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)

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

Course Policies :  Attendance to Lab session is mandatory.

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

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.

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.

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.

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 

Course Policies : Attendance to the lab sessions is mandatory

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

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

Deep Learning is a new approach in Machine Learning which allows to build models that have shown superior performance for a 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 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 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 online tutorials where students can learn technical details of model implementation, training and testing.

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

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)

This course aims at providing a solid and practical algorithmic foundation to the design and use of scalable machine learning algorithms, with particular emphasis on the MapReduce programming model. Students will get familiar with a wide range of topics, through the application of theoretic ideas on problems of practical interest. This is a "reverse class", in which students are required to study (or revise) a particular topic at home, and apply what they have learned solving real world problems, including industrial applications, during numerous laboratory sessions. Laboratory sessions are based on modern technologies such as Jupyter Notebooks.

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

Course Policies: Attendance to Lab sessions is mandatory.

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.

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.

This course provides a solid introduction to European Business law from a managerial and strategic perspective taking an international and comparative approach.The course is the study of how companies manage legal perspectives    in order to create value for the company through corporate and contract issues.The course will focus on the main laws that regulate various aspects of establishing and running a business within the European Union.

Teaching and Learning Methods :

Goals:

- To acquire basic legal knowledge in European Business law

- To have an overview of the company set up process

- To learn about the majors principles of contract law

With cases,  materials  and theoretical approach   

Course Policies :  Course lectures

Adversary style debates

Case Studies

In a globalized world where regions, borders, cultures, men and women, states and firms are getting closer by the day, a minimal understanding of major international issues and current geopolitical challenges (and their many consequences on the daily life of individuals, states, and governments, supranational organizations, and economic actors), no longer appears to be an option but a common sense necessity.

The diversity of interrogations, of major issues and challenges, of the uncertainties structuring the contemporary international environment, lead today’s observers - the citizens, the Chief executive officer as well as students and future managers - to look carefully at those complex and challenging matters. The knowledge and understanding of those issues have become, in a word, indispensable.

Teaching and Learning Methods: Lecture/interactive conference with the class; use of various teaching materials and technical tools (PPT presentations, documentaries, TV programs, internet) to maintain student attention and encourage participation.Course Policies : Attendance and punctuality at all course sessions are mandatory.

Course Policies : Attendance and punctuality at all course sessions are mandatory.

This course provides a solid introduction to European Business law from a managerial and strategic perspective taking an international and comparative approach.The course is the study of how companies manage legal perspectives    in order to create value for the company through corporate and contract issues.The course will focus on the main laws that regulate various aspects of establishing and running a business within the European Union.

Teaching and Learning Methods :

Goals:

- To acquire basic legal knowledge in European Business law

- To have an overview of the company set up process

- To learn about the majors principles of contract law

With cases,  materials  and theoretical approach   

Course Policies :  Course lectures

Adversary style debates

Case Studies

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.

        This course aims to raise students' awareness of the entrepreneurial posture and practice by allowing them to discover the cognitive and operational process that under

lies the transformation of an idea into a viable and sustainable project. 

      Teaching and learning methods:

        The course is organized in the form of a 'full immersion' seminar, which will occupy the students full time for a short week (3 full days + 1 half day). 

       The course will focus on real-life cases.  Students will work in groups of 3 to 4 people, on real projects submitted by the participants. Prior to the course, each participant is asked to think about a topic or an idea that he/she would like to explore further. It can be a real business creation project or an idea that the student would like to explore further. 

        Each participant will present his/her idea in 2 minutes maximum to the whole group during the Ice-Breaker session that will take place during the first morning of the session. Each student will have to choose the project they want to work on to form teams of 3 or 4 people (not all projects can be worked on during the course).

      Course Policy:  Attendance and punctuality to all course sessions is mandatory.

Statistics is a foundation of many areas of science and engineering that involve `` data.’’ This course focuses on fundamental concepts in statistical inference that are necessary for applying statistical methods in practice and that form the basis of other fields such as machine learning.

Teaching and Learning Methods:  Students learn by lectures, exercises, and computer experiments.

Course Policies:  The valid usage of statistical methods requires a mathematical understanding of the underlying mechanism. As such, the course covers both mathematical and algorithmic aspects of statistics.  

The objective of this course is to raise awareness of the threats and challenges and to introduce the main concepts of cybersecurity. The aim is to present a guide of good practices applicable to all IT professionals, whether they are in training or in activity.

• This course provides a broad overview of computer networking, covering the application layer, transport layer, network layer, and link layers.
• It covers basic concepts in computer networking as well as the prominent Internet protocols.

Teaching and learning methods:

Lectures and Lab sessions (group of 2-3 students)

Course  Policies: attendance to labs is mandatory

• Since 1948, the year of publication of Shannon's landmark paper "A mathematical theory of communications", Information theory has paved the ground for the most important developments of today's information/communication world making it perhaps the most important theoretical tool to understand the fundamentals of information technologies.
• Information theory studies the ultimate theoretical limits of source coding and data compression, of channel coding and reliable communications via channels, and provides the guidelines for the development of practical signal-processing and coding algorithms.
• This course covers Information theory at an introductory level.
• The practical implications of theoretical results presented are put in evidence through examples.
• Various perspectives are given to understand every single theoretical results from a intuitive point of view, regardless of your background or study track.

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

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

Database systems are used for storing and maintaining any sort of data, providing convenient access to them through efficient query processing.

This course aims to give fundamental knowledge about databases, with particular attention to relational models. Students will learn how to design a relational database, using established methods such as E/R diagrams, and implement them. In addition, this course will offer an extensive presentation of SQL query languages.

Teaching and Learning Methods:

Lectures (with in-class exercises) and individual practical labs.

Wireless communications are pervasive and have been used for a century. They are used in a very large set of  security applications (communications by security forces, car key remote, alarm system, access control, drone command and control, surveillance devices) . However, day to day applications also require to be protected for privacy and personal security, such as WiFi or mobile communications (2G/3G/4G). At the same a number of challenges are present in wireless communications security, for example, messages are broadcasted, making it possible to intercept them without being noticed. Wireless signals are subject to jamming, making them unavailable.

This course will give a large perspective of the fundamental challenges in securing wireless communications, from the physical layer, modulations to the application protocols. A special focus will be put on practice with hands on exercises (using software defined radios and WiFi dongles).

Teaching and Learning Methods : Course is composed of lectures, Labs and small projects with final presentation.

Course Policies : Class attendance, labs and projects mandatory.

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

Course Policies : Attendance to Lab session is mandatory

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.

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.

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)

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

Course Policies :  Attendance to Lab session is mandatory.

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

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.

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.

This course introduces fundamental concepts in machine learning withapplications to networked systems and Internet of Intelligent Things (IoIT). Students will gain foundational knowledge of cutting edge methods, including autoencoders, deep generative models, and reinforcement learning. They will also get familiar with fundamental information-theoretic frameworks (e.g., information bottleneck) and theoretical principles. We will also introduce large-scale distributed and decentralized learning over wireless networks, in particular under constraints (completion time, radio resources, computational efficiency, etc.). Finally, we highlight key theoretical and practical challenges, together with emerging topics, such as trustworthiness, fairness, and energy efficiency.

Teaching and Learning Methods: Lectures, exercise sessions, and lab sessions. 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 lab session is mandatory. Attendance to lectures and exercise sessions is highly recommended..

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 

Course Policies : Attendance to the lab sessions is mandatory

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

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

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.

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

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)

This course aims at providing a solid and practical algorithmic foundation to the design and use of scalable machine learning algorithms, with particular emphasis on the MapReduce programming model. Students will get familiar with a wide range of topics, through the application of theoretic ideas on problems of practical interest. This is a "reverse class", in which students are required to study (or revise) a particular topic at home, and apply what they have learned solving real world problems, including industrial applications, during numerous laboratory sessions. Laboratory sessions are based on modern technologies such as Jupyter Notebooks.

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

Course Policies: Attendance to Lab sessions is mandatory.

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.