Descriptif
The DataAI study track is a two-year master’s program at the Institut Polytechnique de Paris to prepare students for a PhD. It is concerned with Artificial Intelligence (AI) and large-scale data management. To apply, you can browse the official IP Paris webpage for the the M1 and for the the M2.
The program is taught in English. It teaches students the basics of Machine Learning, Logic, Big Data Systems, and Databases, before diving into applications in advanced machine learning, symbolic AI, swarm intelligence, natural language processing, visual computing, and robotics. Students can choose from a wide variety of courses, including on the mining of large datasets, big data processing systems, reinforcement learning, GPU programming, semantic networks, cognitive modeling, self-organizing multi-agent systems, autonomous navigation for robots, text mining, image understanding, as well as social issues in AI.
The program has a focus on research, and aims to familiarize students from the beginning with scientific work with scientific projects and internships. This way, students are optimally prepared for doing a PhD.
- Language of instruction: English
- ECTS: 120
- Orientation: PhD
- Duration: 1 year (M2) or 2 years (M1+M2)
- Start: September
- Course Location: Quartier Polytechnique, Palaiseau, France
Diplômes concernés
Composition du parcours
- 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