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 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 (reports)
This course introduces fundamental concepts in machine learning with applications to networked systems and the 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.).
Teaching and Learning Methods: Lectures, exercise sessions, and lab sessions. Each lecture starts summarizing key concepts from the 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 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 (reports)
This module addresses mechanisms and strategies to model multi-modal transportation. The objectives are first to introduce concepts of population modeling. Second, it extends graph theory concepts for modeling heterogeneous transportation networks, in particular public transport networks. Mechanisms for activity and demand modeling for multimodal transportation will also be discussed. Finally, this lecture trains on best practices to apply these concepts for efficient multimodal transportation planning on vehicular traffic simulator
Teaching and Learning Methods : Lectures and Lab sessions (group of 2 students)
Course Policies : Attendance to Lab session is mandatory.
This module addresses mechanisms and strategies to improve traffic efficiency and carbon footprint. The objectives are first to introduce the underlying theory such as Waldrop Equilibrium required for efficient path planning. Second, it describes concepts and theory behind the optimization of traffic lights to traffic conditions. Third, it provides guidelines and methodologies to model emissions and integrate them into efficient path planning. Finally, it trains on best practices to apply these concepts for efficient and green path planning on vehicular traffic simulators.
Teaching and Learning Methods : Lectures and Lab sessions (group of 2 students)
Course Policies : Attendance to Lab session is mandatory
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.
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
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. The main objective of this course is to provide an introductory-level coverage of Information Theory. Information theory studies the ultimate theoretical limits of source coding and data compression, channel coding, and reliable communications via channels, and provides the guidelines for the development of practical signal-processing and coding algorithms. The course is meant to provide an intuitive point of view and foundation for both research and practice.
Teaching & Learning Methods: Lectures and homework.
Course policies:
Attendance Policy: Attendance is expected and required in every class period, unless previously discussed with the instructor. We will cover a lot of ground in this course to build a strong foundation for Information Theory, so attendance is important. To encourage learning, preparation, and participation, the lectures will be complemented by additional reading or review materials, e.g., slides, papers, MATLAB codes, and online course materials to cover some of the basic concepts prior to the lectures.
Other Course Policies: All mobile devices (e.g., smartphones, tablets, computers) should be stored securely away during lecture and are not be used unless specifically directed otherwise by the instructor. Use of a mobile device during an exam without the explicit permission of the instructor will be interpreted as the illicit transfer of exam data, will be considered an act of cheating and will be treated as such.
You are expected to approach the instructor with any issue that may affect your performance in class ahead of time. This includes absence from important class meetings, late assignments, inability to perform an assigned task, the need for extra time on assignments, etc. You should be prepared to provide sufficient proof of any circumstances based on which you are making a special request.
Academic Integrity: Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own. Acts that violate this trust undermine the educational process. In this class, all assignments that are turned in for a grade must represent the student’s own work. Submission of any assignment that is in violation of this policy may result in a penalty of an F in the class and may be subject to further disciplinary action.
If you have any questions concerning this policy before submitting an assignment, please ask for clarification.
Human-computer interaction (HCI) is the study of interaction between people (users) and computers, as the intersection of computer science, behavioral sciences, design and several other fields of study. This course aims to provide the basic concepts of user centered design when developing web applications. It will offer a deep dive presentation of modern web technologies: HTML-5, CSS-3 and Javascript. Finally, this course will provide techniques for evaluating user interfaces.
Teaching and Learning Methods: Lectures and mini-project (group of 3-4 students)
Course Policies: Attendance to course session is mandatory.
Course Description:
- Learn and
understand the role of sketching in design and its relation to creativity
- Focus on interaction design for web applications
- Get an overview of existing software tools that support sketching and learn Sketchify or Balsamiq
- Practice
and master new web technologies
- Develop rich web interactive applications (HTML5, CSS3, javascript)
- Learn advanced HTML5 APIs (web audio, media fragments, SEO and semantics)
- Be aware of accessibility constraints and devices
constraints (mobile phone)
- Know how to evaluate user interfaces
- Know your users through user centered design methods
- Learn how to perform A/B tests, usability and ergonomics studies
Bibliography:
- Bill Buxton (2007), Sketching User Experiences: Getting the Design Right and the Right Design, Morgan Kaufmann.
- Marti A. Hearst (2009). Search User Interfaces, Cambridge. http://www.searchuserinterfaces.com/
- Review of User Interface Prototyping Tools, http://www.dexodesign.com/2008/11/07/review-16-user-interface-prototyping-tools/
- HTML EdX online course: https://www.edx.org/xseries/html5-w3c
Requirements:
- Software development methodologies
- Basic knowledge of web technologies (html, css, javscript) is a plus but not mandatory
Reinforcement Learning (RL) has recently emerged as a powerful technique in modern machine learning, allowing a system to learn through a process of trial and error using feedback. It has been succesfully applied in many use-cases, including systems such as AlphaZero, that learnt to master the games of chess, Go and Shogi.
The goal of this course is to introduce the students to basic concepts of RL such as, Markov decision processes, dynamic programming, model-free methods, approximation methods via value function and policy evaluation and many more useful tools. This is a theoretical course but we will provide examples of real-world applications to demonstrate the usefulness of RL.
Teaching and Learning Methods
Lectures, homework, exercises. Each lecture starts summarizing key concepts from previous lecture. Part of each lecture is often dedicated to illustrative examples and exercises.
Course Policies
Attendance to lectures and exercise sessions is not mandatory by highly recommended.