- 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
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.
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.
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.
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 subtitle of this course could be “Multi-Antenna Interference Handling for Multi-User Multi-Cell Systems”. Indeed the main focus is on the exploitation of multiple antennas to (more easily) handle inter-symbol and inter-user interference. Key concepts here are beamforming, MIMO (Multi-Input Multi-Output), Multi-User MIMO, Massive MIMO.
After a basic course in digital communications, a wide range of issues arise in the treatment of physical layer procedures in a wide variety of transmission technologies such as xDSL, gigabit Ethernet, powerline systems, DAB/DVB broadcasting and optical communication systems to name a few. These issues involve e.g. multi-rate echo cancellation for full duplex operation on twisted pair telephone lines, synchronization and equalization techniques in a variety of single and multi-carrier systems, impulsive noise in powerline and automotive systems etc. Even just wireless communications encompass a wide range of systems such as satellite, underwater, near-field communications, fixed wireless access, private systems, sensors, IoT, etc. and a wide range of aspects such as relaying, full duplex radio, cognitive radio, location estimation etc.
Whereas these systems will be briefly mentioned, the main focus will be on cellular wireless and the use of multiple antennas at receivers and transmitters. Spatial filtering, spatiotemporal filtering, and multiuser detection for CDMA are all treated in a unified fashion.
Teaching and Learning Methods: Lectures, Exercise and Lab session (groups of 1-2 students depending on size of class).
Course Policies: Attendance of Lab session is mandatory (25% of final grade).
The aims of the course are to provide students with tools that can help to design error-free software/hardware systems. The course gives both the theoretical foundations and the pratical use of formal methods.
Teaching and Learning Methods : Lectures and Lab sessions.
Course Policies : Attendance to Lab session is mandatory.
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.
Computational methods in digital communications provides a selection of hands-on experiments in programming and implementation techniques for high-performance computing applied to telecommunications. Students will learn architecture concepts and how to optimize software to implement different types of algorithms efficiently on software-based systems.
Teaching methods: The course is primarily given in a lab setting leveraging guided personal work. The instructor gives some lecture material progressively during the course of the lab-sessions in an on-demand fashion. The mini-project teamwork includes regular 45 minutes review meetings with the instructor and teaching assistants to overview progress on the subject.
Grading: 50% final mini-project work and presentation, 50% grading of lab sessions.
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 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.
Image & Video processing is part of many applications related to security: digital watermarking, steganography, image forensics, biometrics, and video surveillance.
- Digital Watermarking allows owners or providers to hide an invisible and robust message inside a digital Multimedia document, mainly for security purposes such as owner or content authentication. There is a complex trade-off between the different parameters : capacity, visibility and robustness.
- Steganographyis the art and science of writing hidden messages (in a picture or a video) in such a way that no-one apart from the sender and intended recipient even realizes there is a hidden message.
- Image Forensics includes two main objectives: (1) To determine through which data acquisition device a given image is generated; (2) To determine whether a given image has undergone any form of modification or processing.
- Biometrics: The security fields uses three different types of authentication : something you know, something you have, ore something you are : a biometric. Common physical biometrics includes fingerprints, hand geometry ; and retina, iris or facial characteristics. Behavioural characters include signature, voice. Ultimately, the technologies could find their strongest role as intertwined and complementary pieces of a multifactor authentication system. In the future biometrics is seen playing a key role in enhancing security, residing in smart cards and supporting personalized Web e-commerce services. Personalization through person authentication is also very appealing in the consumer product area. This course will focus on enabling technologies for Biometrics, with a particular emphasis on person verification and authentication based on or widely using image/video processing.
- Video surveillance is the monitoring of the behavior, activities, or other changing information, usually of people for the purpose of influencing, managing, directing, or protecting. By default, for a better scene understanding, automatic image processing tools are used between acquisition/transmission and visualization or storage
Teaching and Learning Methods: Ce cours comporte un nombre limité de Travaux Pratiques et Travaux Dirigés.
Course Policies: Les TPs sont obligatoires.
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
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.
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.
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 aims at providing a solid and practical foundation to the design and execution of machine learning projects. 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 in numerous laboratory sessions.
To define a machine learning projects, students will use cloud-based GPU resources, such as Google Colab, Kaggle and many more. Typically, projects are developed using Python Notebooks, or a legacy IDE, and can rely on standard machine learning libraries, and available pre-trained models, such as the ones provided by HuggingFace.
Project outcomes take the form of short research-style reports, which include methodological choices, results and a critical discussion on the performance achieved for a particular task.
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