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
- DATA AI-M2 Data & Artificial Intelligence - Master 2
- DATAAI BASICS DATAAI Basics
- CSC_5DA00_TP DATA AI BASICS
- DATAAI Bloc 1 DATAAI Bloc 1
- CSC_5AI30_TP Language Modeling
- APM_5AI18_TP Reinforcement learning
- APM_5DA03_TP Image mining and content-based retrieval
- CSC_5DA02_TP Explainable and Trustworthy AI
- CSC_5DA09_TP Knowledge Base Construction
- APM_5DA12_TP Deep Learning for Computer Vision
- CSC_51056_EP Analyse topologique de données
- CSC_54456_EP Navigation pour les systèmes autonomes
- CSC_51052_EP Visualisation des Données
- CSC_5AI29_TP Language Models and Structured Data
- CSC_52072_EP Graph Machine and Deep Learning for Generative AI
- CSC_52061_EP Randomisation en Informatique : Jeux, Graphes et Algorithmes
- CSC_5AI01_TP Logics and Symbolic AI
- CSC_5DA01_TP Shallow & Deep Learning
- CSC_5SW02_TP Empirical Methods in Software Engineering
- PDV_5DA05_TP Softskills seminar (M2 Only)
- HSS_5DA06_TP AI Ethics
- CSC_5DS32_TP Machine Learning on Structured DATA
- DATAAI Bloc 2 DATAAI Bloc 2
- CSC_52087_EP Apprentissage profond avancé
- CSC_5IA10_TA Game theory and multi-agent control
- CSC_54656_EP Procédure de décision pour l'intelligence artificielle
- APM_5DA13_TP Representation Learning for Computer Vision and Medical Imaging
- CSC_5AI32_TP Privacy-Preserving Data Analytics
- CSC_5IA05_TA Apprentissage pour la robotique
- APM_5DA01_TP Multi-Agent Systems
- Crédits libres M2 DATAAI 5 Crédits libres M2 DATAAI
- LFR_9N042_TP FR-MUTUALISE-S2
- LFR_9N041_TP FR-MUTUALISE-S1
- CSC_53432_EP Large Language Models
- Projets M2 DATAAI Projets M2 DATAAI
- PRJ_5DA14_TP Projet 1
- PRJ_5IP91_TP PhD Track Research Project
- PRJ_5DA15_TP Projet 2
- Stages M2 Stages M2
- INT_5IP35_TP Stage de Master 2 IP Paris
- CSC_5AI07_TP Programming with GPU for Deep Learning
- CSC_5AI31_TP Advanced Topics in Large Language Models
- CSC_51054_EP Apprentissage automatique et apprentissage profond
- CSC_52081_EP Apprentissage par renforcement et Agents Autonomes
- ECE_5ST14_TP Linux embarqué
- ECE_5ST38_TP IA frugale sur FPGA
- DATAAI BASICS DATAAI Basics
