Objectif
Regarding the objectives of this Master, here is the link to consult
https://dataai.jachiet.com/
The master’s program will equip students with the fundamental knowledge, technical skills and concrete applied methodologies for making machines more intelligent. In particular, students will acquire experience in using and developing data-supported smart services and tools for data-driven decision making and will learn how to master technical and scientific challenges in processing large data and knowledge. The students will be taught to solve theoretical problems as well as applied ones, to present their work both in oral presentations and in written reports, to analyze the bibliography and identify open research directions, to work independently as well as in a team, to identify and seek appropriate resources for advancing their work, whether theoretical or applied, and to take initiatives.
domaines d'enseignement
Informatique.compétences acquises
Regarding the aims of this Master, here is the link to consult
https://dataai.jachiet.com/
The combination of big data and artificial intelligence in all of its forms is an active field of research. Students will be prepared for research in Robotics, Image processing, Machine Learning, Web technologies, the Social Web, Data Analytics, Big Data Management, Knowledge Base Management, Information Extraction, Information Retrieval, Databases, Data Warehousing, Knowledge Representation, and Distributed Data Management. Students who wish to pursue a PhD afterwards are more than encouraged to do that. The Institut Polytechnique de Paris and the associated research labs (INRIA, CNRS, etc.) offer a great environment for a PhD, and our program is an optimal preparation for this path. The program will also allow students to apply to positions in the industry, mostly in research and development labs.
Parcours
- M1DATAAIM1 Master 1 - Data and Artificial Intelligence
- Choix DATAAI BASICS Choix de cours dans le block DATAAI Basics
- CSC_5DA00_TP DATA AI BASICS
- Choix de cours ETHICS Choix de cours dans le bloc Ethics
- HSS_5DA06_TP AI Ethics
- Choix MACHINE LEARNING Choix de cours dans le block Machine Learning
- CSC_5DA01_TP Shallow & Deep Learning
- CSC_52081_EP Apprentissage Automatique Avancé et Agents Autonomes
- CSC_52087_EP Advanced Deep Learning
- Choix de cours LOGICS Choix de cours dans le bloc Logics
- Choix BIG DATA SYSTEMS Choix de cours dans le bloc Big Data Systems
- ECE_5DA04_TP Big Graph Databases
- CSC5003 Infrastructures d'analyse de données
- Choix de cours DATABASES Choix de cours dans le bloc Databases
- CSC_4SD02_TP Databases / Bases de données
- CSC_0EL13_TP Databases (créneau D)
- CSC_51053_EP Système de Gestion de Base de Données
- Choix FULLY OPTIONAL COURSES Choix de cours dans le bloc Fully Optional Course
- APM_5AI27_TP Large-scale Generative Models for NLP and Speech Processing
- MOB_0AT09_TP Social Emergence in Complex Systems (Télécom Paris - Palaiseau)
- CSC_4SD01_TP Exploration de données /Data Mining
- APM_5AI07_TP Programming with GPU for Deep Learning
- APM_5AI25_TP Algorithmic information and artificial intelligence
- CSC_4SD04_TP Graph Learning
- CSC_5AI12_TP Natural Language Processing
- CSC_5DS17_TP Multimodal Dialogue
- CSC_4SD05_TP Cognitive approach to Natural Language Processing (Option Gestion des données)
- APM_5AI18_TP Reinforcement learning
- APM_5AI04_TP Modèles probabilistes et apprentissage automatique
- APM_5DA10_TP Basics of image processing and analysis
- APM_5DA03_TP Image mining and content-based retrieval
- CSC_5DA02_TP Explainable and Trustworthy AI
- CSC_5DA11_TP Data Science in Practice
- CSC_5DA07_TP Collective Intelligence
- CSC_5DA09_TP Knowledge Base Construction
- APM_5DA12_TP Deep Learning for Computer Vision
- APM_5DA13_TP Representation Learning for Computer Vision and Medical Imaging
- ENSTA-INF656L Decision Procedures for Artificial Intelligence
- MAP654I Practical introduction to machine learning
- CSC_5DS29_TP Fighting Online Information Risks
- CSC_51056_EP Analyse de Données Topologiques
- CSC_52082_EP Introduction à la Fouille de Textes et au Traitement Automatique des Langues(NLP)
- CSC_54441_EP Introduction à la vérification des réseaux neuronaux
- CSC_54456_EP Navigation pour les systèmes autonomes
- CSC_52002_EP Computer Vision: from Fundamentals to Applications
- Projets M1 DATAAI Projets M1 DATAAI
- Crédits libres M1 DATAAI 5 crédits libres à choisir en M1 DATAAI
- X-INF569 Decision Theory with Applications to Energy Systems
- LFR_9N041_TP FR-MUTUALISE
- X-INF567 Wireless Networks from cellular to connected objects
- X-LANG.ALL B1 Cours d'Allemand B1
- MAP578 Emerging Topics in Machine Learning
- X-INF561 Randomization in Computer Science : Games, Networks, Epidemic and Evolution
- X-MAP554C Elements of Functional Analysis and Mesure Theory
- X-LAN576FLE Cours de langue
- X-MAP576N Advanced Learning for Text and Graph Data
- X-INF643 Softs Robots : Stimulation, Fabrication and Control
- PRJ_5IP91_TP PhD Track Research Project
- MAP670L Generalisation properties of algorithms in ML
- X-PHY580 Quantum information : entanglement, control and platforms for quantum technologies
- X-MAP583 Deep Learning (Polytechnique)
- X-INF473V Modal d'informatique - Deep Learning in Computer Vision
- X-INF513 Projet de 3a Algorithmes et fondements des languages de programmation
- X-INF587 Informatique quantique et applications
- X-ECO555 Théorie des jeux
- X-INF513B Projet de 3a Algorithmes et fondements des languages de programmation P2
- TSP-CSC5003-1 Big Data Infrastructures
- TSP-CSC5003-2 Semantic Networks
- ENSTA-IA302 Constraint Programming
- ENSTA-IA303 Efficient resolution of logical models
- X-INF473A Modal d'informatique - Algorithms and Competitive Programming
- X-BIO557 Neurosciences
- Stages M1 Stages M1 DATAAI
- Stages PhD Track M1 Stages PhD Track M1
- Choix DATAAI BASICS Choix de cours dans le block DATAAI Basics
