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TP_UE_27070 2025/2026 - CSC_4GI06_TP - Réseaux mobiles et IOT

Ce cours est consacré aux évolutions des architectures des réseaux mobiles et de l'Internet des Objets (IoT), et leur intégration.

Ce cours vise à décrire les piles de protocoles utilisées dans les réseaux mobiles, avec un accent sur les fonctions de réseaux coeurs mobile (packet core), les procédures utilisées pour la gestion de la mobilité (roaming), facturation, etc.

Ensuite, les étudiants pourront également appliquer les connaissances acquises jusqu'à présent dans la filière GIN dans le contexte de l'internet des objets, et comprendre les différences architecturales et protocolaires qu'il a fallu introduire dans ce contexte spécifique. En particulier, l'accent sera mis sur l'utilisation de IPv6 avec 6LowPAN et la comparaison d'applications REST (CoAP) ou de type pub-sub (MQTT) dans les environements contraints.
Finallement, les approches combinées comme HetNet (réseaux hétérogènes), connectivité duale et les communications multi-chemins seront introduites.
Catégorie: 2A

TP_UE_27095 2025/2026 - HSS_0EL32_TP - Internet and society

Course leader: Antonio CASILLI.
Tutor: Elinor WAHAL

This course provides a training in sociology of social media and an exploration on the social impact of information and communication technologies. You will acquire knowledge of key concepts of social sciences applied to digital technologies: identity, community, social networks, social capital, work/cooperation, norm, privacy, public space, social structures, collective action ...

This course requires you to be fluent in English (written and spoken) as your assessment will be carried out via oral exposés, readings, reports, online contribution, and applied exercises.

The ultimate goal is to allow you to develop a critical distance from your own use of digital technologies, which will be systematically transferable to professional context.

--- WARNING: this course requires attendance and participation as a specific course objective. ---

Catégorie: 2A

TP_UE_27229 2025/2026 - MOB_0AT09_TP - Social Emergence in Complex Systems (Télécom Paris)

PREREQUISITES:

All lectures and all materials are in English, so we expect students to be fluent in English. Lab work sessions are based on software written in Python. Mastery of the Python language is not required, but students who attend this course will be fluent in procedural object-oriented programming (Java, C++, Python or equivalent). If needed, they will get some knowledge of Python by themselves before the Athens week. Some fluency in basic mathematics (e.g optimization) is required.

OBJECTIVES:

Insect colonies, evolving species, economic communities, social networks are complex systems. Complex systems are collective entities whose interactions lead to emerging behaviour. Though the detailed interactions between all agents are too complex to be described, their collective behaviour often obeys much simpler rules. The objective of this course is to present some of the laws that control emergent behaviour and may be used to predict it. The course will address conceptual issues, at the frontiers between biology, sociology and engineering.

Students who have a scientific curiosity for emerging phenomena in nature (evolution of species, self-organizing collective behaviour) and are interested in importing ideas from nature to engineering are welcome to this course.

PROGRAMME TO BE FOLLOWED:

The main topics studied in this module are:

Swarm intelligence; feedback loops; Emerging phenomena; definition of emergence; biological evolution; genetic algorithms; punctuated equilibria; scale invariance; implicit parallelism; autocatalytic phenomena; multi-scale systems;

cooperation; altruism; social signalling.

Potential applications are robotic swarms, smart grids, smart cities, autonomous car networks, ecology and biodiversity, crowd behaviour, financial markets, social norms, mob phenomena on social media, ...

COURSE EXAM:

The pedagogy consists in alternating lectures and practical work on machines. Each afternoon consists in a lab work session in which students will get an intuitive and concrete approach to phenomena such as genetic algorithms, ant-based problem solving, collective decision, cultural emergence or sex ratio in social insects.

Students are asked to use the software platform that is provided to them and to perform slight modifications. They will study emergent phenomena by themselves and develop their own personal (micro-)project.

Students will be evaluated based on the following tasks:

- Answers during Lab sessions

- Small open question quiz

- A 5 min. presentation of their personal project

- A short written description of their personal project (+ source files)

Catégorie: 2A

TP_UE_27555 2025/2026 - MOB_0AT20_TP - From cloud datacenters to mobile edge computing (MEC): architectures, innovative services, and open challenges (Télécom Paris)

OBJECTIVES OF THE COURSE:

 

In a first step, we propose a brief state of the art on CPU and hardware architectures. The performance criteria of a blade server (the basic computing unit within a datacenter) are analyzed (CPU speed, multithread vs. multicore, energy consumption etc.). We describe then into details the rationale of the typical 2D-fat-tree optical backplane network architecture of a datacenter. The pros and cons of this architecture are discussed in reference to the potentialities of all-optical technologies. The basic rationale of virtual machines (VM) assignment to physical machines (PM) and migration strategies is presented via bin packing theory. After these preliminary considerations, the evolution from hypervisors to Virtual Containers (VC) such as Kubernetes and Docker is briefly discussed. An original focus is dedicated to the impact of heat dissipation onto real-time VM migrations. In this perspective, we show how a Pareto optimization approach can be considered. We conclude this first section by a brief presentation of Cloud standardization bodies and the three Cloud business models considered up to now.

 

The second part of this course deals with the most innovative aspect of the Cloud domain, namely Cloud Radio Access Networks (CRAN) and Mobile Edge Computing (MEC). CRAN refers to the innovative access infrastructure that will enable to deport Cloud intelligence close to fix/mobile end-users, including IoT sensors/actuators. Autonomous vehicles (AV) and more extensively, Intelligent Transportation Systems (ITS), the first two pillar applications of 5G, rely on Cloud and IoT. In this perspective, we depict into details the basic principles of Digital Radio-over-Fiber (D-RoF) and the two underlying standards, namely CPRI and OBSAI. In our conclusion, we depict the open perspectives offered by 5G/MEC for future intelligent transportation systems (ITS).

 

 

 

Catégorie: 2A

TP_UE_27579 2025/2026 - APM_5DA13_TP - Representation Learning for Computer Vision and Medical Imaging

Good and expressive data representations can improve the accuracy of machine learning problems and ease interpretability and transfer. For vision tasks, handcrafting good data representations, a.k.a. feature engineering, was traditionally hard. Deep Learning has changed this paradigm by allowing to automatically discover good representations from data. This is known as representation learning. The objective of this course is to provide an introduction to representation learning in computer vision and medical imaging applications. 

We will cover the following subjects: 

- Introduction to Representation Learning for Vision  

- Transfer Learning and Domain Adaptation 

- Self-supervised and Contrastive Learning 

- Knowledge Distillation 

- Disentangled Representations 

- Conditional Generative models 

- Attention and Transformers  

- Visualisation and interpretability in Neural Networks 

- Multimodal representation learning and Foundation models  

Catégorie: 3A

TP_UE_27608 2025/2026 - IME_0EL31_TP - Finance d’entreprise

Intervenant : Arnaud Maréchal
Actions, bilan, dette, levée de fonds, compte de résultat, résultat net, immobilisations… Si ces notions ne sont pas a priori très sexy, elles sont pourtant déterminantes dans le fonctionnement des entreprises dans lesquelles vous évoluerez. Pourquoi précisément le cours d’une entreprise monte-t-il ? Pourquoi une entreprise « dépose son bilan » ? Pourquoi certaines entreprises sont rachetées des milliards sans générer le moindre euro de bénéfice ? Ce cours permet de comprendre les mécanismes de base de ces fonctionnements financiers. Il permet de comprendre la logique de finance d’entreprise et de comptabilité, et de relier cette mécanique aux enjeux stratégiques des entreprises. Le cours articule des analyses de cas, des cours expliquant la mécanique comptable et financière, des exercices sur des états financiers de grandes entreprises (notamment du numérique).
Catégorie: 2A

TP_UE_27616 2025/2026 - APM_5AI26_TP - Kernel Machines

Kernel Machines
Synopsis
1- Notions on Kernels and Reproducing Kernel Hilbert Space Theory
2- Kernel machines for regression, classification and dimensionality reduction
3- Kernel design and kernel learning
4- Kernel Machines for structured output prediction
5- Scaling up kernel machines
6- Brief overview of relationships between kernel machines and neural networks
The goal of this  course is to introduce and deepen kernel methods as a major tool in nonparametric approaches to machine learning.
  In this course you will (re)discover that linear methods extend to nonlinear by using the famous kernel trick. Linear regression, linear classification and linear dimensionality reduction approaches  will be highlighted as typical examples of this  family of  approaches. You will also learn how to think about machine learning in terms of hypothesis spaces and regularization choices, by leveraging a unique hyperparameter: the kernel. This will raise the issue of kernel  design and learning. We will then show that the kernel trick is  also interesting in the output space by tackling multi-task and structured prediction.
Eventually, we will present an overview of the still-open question of scaling up kernel methods and will discuss the links between kernel  machines and neural networks.
The course is punctuated by 3 practical sessions (3H00, 1H30, 1H30).
Catégorie: 3A
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