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, Données, Intelligence Artificielle.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
- Choix FULLY OPTIONAL COURSES Choix de cours dans le bloc Fully Optional Course
- APM_5AI04_TP Modèles probabilistes et apprentissage automatique
- APM_5AI18_TP Reinforcement learning
- APM_5DA03_TP Image mining and content-based retrieval
- APM_5DA10_TP Basics of image processing and analysis
- APM_5DA12_TP Deep Learning for Computer Vision
- APM_5DA13_TP Representation Learning for Computer Vision and Medical Imaging
- CSC_4SD04_TP Graph Mining
- CSC_4SD05_TP Cognitive approach to Natural Language Processing (Option Gestion des données)
- CSC_5AI12_TP Natural Language Processing
- CSC_5DA02_TP Explainable and Trustworthy AI
- CSC_5DA07_TP Collective Intelligence
- CSC_5DA09_TP Knowledge Base Construction
- CSC_5DA11_TP Data Science in Practice
- CSC_5DS17_TP Multimodal Dialogue
- CSC_5DS29_TP Fighting Online Information Risks
- APM_5AI26_TP Kernel Machines
- CSC_51052_EP Visualisation des Données
- CSC_51056_EP Analyse topologique de données
- CSC_52002_EP Computer Vision: from Fundamentals to Applications
- 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_54656_EP Procédure de décision pour l'intelligence artificielle
- CSC_5AI07_TP Programming with GPU for Deep Learning
- CSC_5AI25_TP Algorithmic information and artificial intelligence
- CSC_5AI27_TP Large-scale Generative Models for NLP and Speech Processing
- CSC_5AI29_TP Language Models and Structured Data
- Choix FULLY OPTIONAL COURSES Choix de cours dans le bloc Fully Optional Course
