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M1 Artificial Intelligence

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  • Places available
    20
  • Language(s) of instruction
    English
Présentation
Objectives

The rapid growth of Artificial Intelligence (AI) research and applications offers unprecedented opportunities. This course is intended for students wishing to receive a good basic education covering a broad spectrum of concepts and applications of data-driven AI and learning by example.

The program offers introductory courses in statistical learning, deep learning and reinforcement learning, optimization, signal processing, information theory and game theory. Numerous options make it possible to perfect oneself in learning theory, and to specialize in many fields such as massive data processing, image and language processing.

Location
ORSAY
GIF SUR YVETTE
Course Prerequisites

This course requires good bases in mathematics and computer science:

  • Probability and statistics
  • Linear algebra
  • (optional) Differential and integral calculus
  • Scientific programming
  • Visualization of the data
Additional information

More information on our website: https://ai-master.lisn.fr/

Skills
  • Know the basics of applied statistics and optimisation.

  • Be able to handle big data.

  • Differentiate and appropriately apply supervised learning, unsupervised learning, and reinforcement learning.

  • Program predictive models using Python, and learn to use scikit-learn.

  • Visualise data and illustrate results using programming tools.

  • Write a project proposal and communicate results both in writing and orally.

Post-graduate profile

Students who have followed this course will have a solid theoretical and practical training. The many projects and internships will ensure that they are well prepared to enter the workforce. This master's degree opens towards research and teaching as well as to careers in the industrial sector or services.

Career prospects

This course prepares to research and R&D professions in new fields of application in full swing: computer vision (autonomous vehicles and biometrics); voice recognition (necessary for new human-machine interfaces for smartphones); filtering and aggregation of heterogeneous and textual content (essential to commercial solutions for managing big data streams); management and monitoring of complex or critical industrial systems that rely on data analysis.

Continuation of studies: at the end of the course, the students can prepare a doctorate within the ED STIC of the University Paris-Saclay by joining one of the research laboratories of the site or within an R & D department of a company.

Professional integration: graduates can work in companies developing innovative software, startups or integrate R & D departments of companies such as Thales, Orange, HP, IBM Research, Yahoo, Google, Facebook. Graduates can also work in public research and academia.

Collaboration(s)
Laboratories
  • Laboratoire Interdisciplinaire des Sciences du Numérique (LISN, ex-LIMSI et ex-LRI)
  • Laboratoire Méthodes Formelles (LMF, ex-LSV et ex-LRI)
  • Laboratoire des Signaux et Systèmes (L2S)
Programme

Ce parcours-type est proposé en deux voies d'apprentissage :
- La voie initiale (AI)
- La voie EIT Digital (EIT - DS)

Toutes les UEs listées ci-dessous devront être validées au cours du parcours (M1 / M2): il est requis d'acquerir 60 ECTS par niveau, pour un total de 120 ECTS à l'issue des deux années.
Pour valider le parcours-type AI (M1 & M2) dans la voie initiale, les étudiants devront suivre :

* En M1 - AI :
- 4 UE dont l'intitulé est [AI] PREi : xxx (pour un total de 10 ECTS)
- 4 UE dont l'intitulé est [AI] TCi : xxx (pour un total de 10 ECTS)
- 6 UE dont l'intitulé est [AI] OPTi : xxx (pour un total de 15 ECTS)
- Les 6 UE de soft skills suivantes (pour un total de 15 ECTS) :
* Soft skills 1A (Langue)
* Soft skills - Seminars A & B
* Soft skills - Projects A & B
* Soft skills - Summer school
- l'UE TER Stage (10 ECTS)

* En M2 - AI :
- 2 UE dont l'intitulé est [AI] OPTi ou 2 UE intitulées [AI] PREi pour ceux qui ne les ont pas encore pris (5 ECTS)
- 2 UE intitulées [AI] TCi et 4 UE intitulées [AI] OPTi (15 ECTS) ou les 6 cours [AI] TCi obligatoires pour ceux qui ne les ont pas encore pris
- 2 UE intitulées [AI] OPTi (5 ECTS)
- 2 UE intitulées Soft skills - xxx
- Un stage long (en laboratoire ou entreprise) au 2ème semestre du M2 pour 30 ECTS

Le choix des étudiants pour les UE [AI] OPTi pourra être complété avec les UE suivantes :
- [DS] Social and Graph Data Management
- [DS] Algorithms for Data Science
- [DS] Distributed Systems for Massive Data Management

Pour valider le parcours-type AI (M1 & M2) dans la voie EIT - DS, les étudiants devront suivre :

* En M1- EIT DS :
- [AI] PRE 1, [AI] PRE2 et [AI] PRE4 7.5 ECTS
- [AI] TC1, [AI] TC2, [AI] TC3 et [AI] TC 5 10 ECTS
- 4 UE intitulées [AI] OPTi 10 ECTS
- Les UE intitulées EIT -xxx (pour un total de 20 ECTS); Attention, l'UE EIT - Innovation & Entrepreneurship Thesis est à suivre en M2
- UE Soft skills - Summer School (4 ECTS)
- UE Soft skills - Transversal project A & B (5 ECTS)
- UE French Language and Culture 1 (2 ECTS)

* En M2 - EIT DS :
- UE EIT - Innovation & Entrepreneurship Thesis (6 ECTS)
- UE French Language and Culture 2 (2 ECTS)
- Un stage long (30 ECTS).

Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
[AI] OPT 10: IMAGE INDEXING AND UNDERSTANDING 2.5 15 6
[AI] OPT1 : GRAPHICAL MODELS 2.5 15 6
[AI] OPT14:MULTILINGUAL NATURAL LANGUAGE PROCESSING 2.5 21
[AI] OPT2: IMAGE PROCESSING 2.5 21
[AI] OPT3 : REINFORCEMENT LEARNING 2.5 15 6
[AI] OPT4: DEEP LEARNING 2.5 10.5 10.5
[AI] OPT5 : VOICE RECOGNITION AND AUTOMATIC LANGUAGE PROCESSING 2.5 21
[AI] OPT8: GAME THEORY 2.5 12 4.5 4.5
[AI] OPT9: DATA CAMP 2.5 10 15
[AI] PRE1: APPLIED STATISTICS 2.5 10.5 10.5
[AI] PRE2: MATHEMATICS FOR DATA SCIENCE 2.5 12 4.5 4.5
[AI] PRE3: DATACOMP 1 2.5 12 9
[AI] PRE4: SCIENTIFIC PROGRAMMING 2.5 9 12
[AI] TC0 : Introduction to Machine Learning 2.5 15 6
[AI] TC2: OPTIMIZATION 2.5 12 4.5 4.5
[AI] TC4: Probabilistic Generative Models 2.5 16.5 4.5
[AI] TC5: SIGNAL PROCESSING 2.5 24
[AI] TC6: DATACOMP 2 2.5 12 9
[ANO] Blockchain 2.5
[ANO] Evaluation de performances 2.5
[ANO] Internet of Things 2.5 21
[ANO] Optimisation dans les graphes 2.5 21
[ANO] Optimisation discrète non linéaire 2.5 21
[ANO] Optimisation multi-objectifs 2.5 21
[ANO] MPI programming 2.5
[ANO] Programmation système et réseaux 2.5 21
[ANO] Réseaux mobiles 2.5 21
[ANO] Réseaux sans fil 2.5 21
[ANO] Tests fonctionnels de protocoles 2.5 21
[ANO] Théorie des jeux 2.5 21
[ANO] Virtualisation et cloud 2.5
[DS] Algorithms for Data Science 2.5 12 9
[DS] Bases de données avancées I : Optimisation 2.5 9 8 4
[DS] Bases de données avancées II : Transactions 2.5 9 8 4
[DS] Data Science Project 2.5 3 18
[DS] Distributed Systems for Massive Data Management 2.5 12 0 9
[DS] Intelligence Artificielle, Logique et Contraintes 2.5 10.5 10.5
[DS] Intelligence Artificielle, Logique et Contraintes : Projet 2.5 10.5 10.5
[DS] Knowledge Discovery in Graph Data 2.5 12 6 3
[DS] Semantic Web and Ontologies 2.5 12 9
[DS] Social and Graph Data Management 2.5 12 9
[HCI] Advanced Design of Interactive Systems 2.5
[HCI] Advanced Immersive Interactions 2.5 21
[HCI] Advanced Immersive Interactions - Project 2.5
[HCI] Advanced Programming of Interactive Systems 1 2.5
[HCI] Advanced Programming of Interactive Systems 2 2.5
[HCI] Career Seminar - Level 1 2.5
[HCI] Career Seminar - Level 1 : Project 2.5 21
[HCI] Career Seminar - Level 2 2.5
[HCI] Career Seminar - Level 2 project 2.5
[HCI] Creative Design 2.5
[HCI] Creative Design : Project 2.5
[HCI] Design of Interactive Systems 2.5
[HCI] Design project - Level 1 2.5 21
[HCI] Design project - Level 1 : Project 2.5 21
[HCI] Design project - Level 2 2.5 21
[HCI] Design project - Level 2 : Project 2.5 21
[HCI] Digital Fabrication 2.5
[HCI] Digital fabrication : Project 2.5
[HCI] Evaluation of Interactive Systems 2.5
[HCI] Experimental Design and Analysis 2.5
[HCI] Fundamental of Human-Computer Interaction 1 2.5
[HCI] Fundamental of Human-Computer Interaction 2 2.5
[HCI] Fundamental of situated computing 2.5
[HCI] Fundamentals of eXtended Reality 2.5
[HCI] Gestural and Mobile Interaction 2.5
[HCI] Groupware and Collaborative Work 2.5 21
[HCI] Groupware and Collaborative Work : Project 2.5 21
[HCI] Interactive Information Visualization 2.5
[HCI] Interactive Information Visualization : Project 2.5
[HCI] Interactive Machine Learning 2.5
[HCI] Interactive Machine Learning : Project 2.5
[HCI] Mixed Reality and Tangible Interaction 2.5 21
[HCI] Mixed Reality and Tangible Interaction - Project 2.5 21
[HCI] Programming of Interactive Systems 1 2.5
[HCI] Programming of Interactive Systems 2 2.5
[HCI] Serious games 2.5
[HCI] Serious games : project 2.5
[HCI] Studio Art Science 2.5 21
[HCI] Virtual Humans 2.5 21
[HCI] Virtual Humans : Project 2.5 21
[ISD] Algorithmique avancée 3 25
[ISD] Algorithmique distribuée 3 25
[ISD] Anglais 3 25
[ISD] Anglais 3 25
[ISD] Blockchain 3 25
[ISD] Cloud Computing 3 25
[ISD] Communication 3 25
[ISD] Data Lake 3 25
[ISD] Data Warehouse I 3 25
[ISD] Data Warehouse II 3 25
[ISD] Droit informatique 3 25
[ISD] Extraction et programmation statistique de l'information 3 25
[ISD] Introduction à l'apprentissage 3 25
[ISD] IoT (Internet des objets) 3 25
[ISD] langages Dynamiques 3 25
[ISD] Machine learning/Deep learning 3 25
[ISD] Mémoire 12 8
[ISD] Modèles Mathématiques 3 25
[ISD] Modélisation 3 25
[ISD] Optimisation 3 25
[ISD] outils pour la manipulation et l'extraction de données 3 25
[ISD] Politiques et concepts avancés en sécurité 3 25
[ISD] Probabilités/Statistiques 3 25
[ISD] Programmation système et réseau 3 25
[ISD] Projet étude de cas 3 25
[ISD] Projets tuteurés 6 25
[ISD] Rapport d'activité 6 5
[ISD] Représentation des connaissances et visualisation 3 25
[ISD] Réseaux 3 25
[ISD] Réseaux sans fil 3 25
[ISD] sécurité 3 25
[ISD] Services et applications Web 3 25
[ISD] Test et Vérification 3 25
[ISD] Traitement automatique des langues 3 25
[ISD] Traitement distribué des données. 3 25
[QDCS] Algorithms in the nature 2.5 21
[QDCS] Robust distributed algorithms 2.5 21
[QDCS] Parallel algorithms 2.5 12 6 3
[QDCS] Auto-stabilisation 2.5 21
[QDCS] Big Data 2.5 12 3 8
[M1 QDCS] High performance computing 2.5 12 9
[QDCS] [M2 QDCS] Recent trends in parallel, distributed, and quantum computing 2.5 21
[QDCS] Initiation to quantum algorithms and programming 2.5 21
[M1 QDCS] Game, learning, and optimisation of 2.5 21
complex systems 2.5 21
[QDCS] Stochastic optimisation 2.5 21
[QDCS] Ordonnancement et systèmes d'exécution 2.5 21
[QDCS] Advanced C++ programming 2.5 9 0 12
[QDCS] GPU programming 2.5 12 9
[QDCS] Programmation orientée objet 2.5 11 10
[SOFT] Soft skills - 1A (Langue) 2.5 21
[SOFT] Soft skills - 1B (Langue) 2.5 100
[SOFT] Soft skills - 2 (Communication) 2.5 21
[SOFT] Soft skills - 3 (Formation à la vie de l'entreprise - Initiation) 2.5 21
[SOFT] Soft skills - 4 Innovation et Entreprenariat 2.5 21
[SOFT] Soft skills - 5 Innovation et Entreprenariat avancé 2.5 21
[SOFT] Soft skills - Seminars (Fairness in Data Science) 2.5 20
[SOFT] Soft skills - Seminars B 2.5
[SOFT] Soft skills - Summer school 2.5 21
[SOFT] Soft skills - Transversal Project A 2.5 7 7 7
[SOFT] Soft skills - Transversal Project B 2.5 7 7 7
EIT - Business Development Lab 1 4
EIT - Business Development Lab 2 5
EIT - Innovation & Entrepreneurship Advanced 1 2.5 21
EIT - Innovation & Entrepreneurship Advanced 2 2.5
EIT - Innovation & Entrepreneurship Study 1 3 21
EIT - Innovation & Entrepreneurship Study 2 3
EIT - Innovation and Entrepreneurship Basics 1 3
EIT - Innovation and Entrepreneurship Basics 2 3
EIT - Summer School 4
French Language and Culture 1 2 30
French Language and Culture 2 2 21
Long internship 30

Ce parcours-type est proposé en deux voies d'apprentissage :
- La voie initiale (AI)
- La voie EIT Digital (EIT - DS)

Toutes les UEs listées ci-dessous devront être validées au cours du parcours (M1 / M2): il est requis d'acquerir 60 ECTS par niveau, pour un total de 120 ECTS à l'issue des deux années.
Pour valider le parcours-type AI (M1 & M2) dans la voie initiale, les étudiants devront suivre :

* En M1 - AI :
- 4 UE dont l'intitulé est [AI] PREi : xxx (pour un total de 10 ECTS)
- 4 UE dont l'intitulé est [AI] TCi : xxx (pour un total de 10 ECTS)
- 6 UE dont l'intitulé est [AI] OPTi : xxx (pour un total de 15 ECTS)
- Les 6 UE de soft skills suivantes (pour un total de 15 ECTS) :
* Soft skills 1A (Langue)
* Soft skills - Seminars A & B
* Soft skills - Projects A & B
* Soft skills - Summer school
- l'UE TER Stage (10 ECTS)

* En M2 - AI :
- 2 UE dont l'intitulé est [AI] OPTi ou 2 UE intitulées [AI] PREi pour ceux qui ne les ont pas encore pris (5 ECTS)
- 2 UE intitulées [AI] TCi et 4 UE intitulées [AI] OPTi (15 ECTS) ou les 6 cours [AI] TCi obligatoires pour ceux qui ne les ont pas encore pris
- 2 UE intitulées [AI] OPTi (5 ECTS)
- 2 UE intitulées Soft skills - xxx
- Un stage long (en laboratoire ou entreprise) au 2ème semestre du M2 pour 30 ECTS

Le choix des étudiants pour les UE [AI] OPTi pourra être complété avec les UE suivantes :
- [DS] Social and Graph Data Management
- [DS] Algorithms for Data Science
- [DS] Distributed Systems for Massive Data Management

Pour valider le parcours-type AI (M1 & M2) dans la voie EIT - DS, les étudiants devront suivre :

* En M1- EIT DS :
- [AI] PRE 1, [AI] PRE2 et [AI] PRE4 7.5 ECTS
- [AI] TC1, [AI] TC2, [AI] TC3 et [AI] TC 5 10 ECTS
- 4 UE intitulées [AI] OPTi 10 ECTS
- Les UE intitulées EIT -xxx (pour un total de 20 ECTS); Attention, l'UE EIT - Innovation & Entrepreneurship Thesis est à suivre en M2
- UE Soft skills - Summer School (4 ECTS)
- UE Soft skills - Transversal project A & B (5 ECTS)
- UE French Language and Culture 1 (2 ECTS)

* En M2 - EIT DS :
- UE EIT - Innovation & Entrepreneurship Thesis (6 ECTS)
- UE French Language and Culture 2 (2 ECTS)
- Un stage long (30 ECTS).

Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
[AI] OPT 11: DEEP LEARNING FOR NLP 2.5 18 3
[AI] OPT 12: INFORMATION EXTRACTION FROM DOCUMENTS TO INTERFACES 2.5 10.5 10.5
[AI] OPT 13: Theorie de l'information 2.5 10.5 10.5 0 0
[AI] OPT 15: Fairness in AI 2.5 10.5 10.5
[AI] OPT 16: Creation of an AI challenge 2.5 10.5 10.5
[AI] OPT 17: Hands-on NLP 2.5 10.5 10.5
[AI] OPT6: LEARNING THEORY AND ADVANCED MACHINE LEARNING 2.5 21
[AI] OPT7: ADVANCED OPTIMIZATION 2.5 12 4.5 4.5
[AI] TC1: MACHINE LEARNING 2.5 15 6
[AI] TC3: INFORMATION RETRIEVAL 2.5 9 12
[SOFT] Soft skills - Computer Sciences & Sustainable Development 2.5 9 12
Modalités de candidatures
Application period
From 22/03/2024 to 20/04/2024
Compulsory supporting documents
  • Motivation letter.

    (* Describe a personal experience that convinced you to pursue in AI studies. * Which classes you enjoyed in the past could be relevant to AI * What are your favorite AI topics ? * How do you see your future career as an AI graduate)
  • Letter of recommendation or internship evaluation.

  • Document at your convenience.

    (List of applications for other tracks, in particular in the Paris-Saclay computer science master's program, in order of preference.)
  • Completed questionnaire (to download on the master's web page).

    (Auto-evaluation form to be downloaded from https://guyon.chalearn.org/teaching/ai-master)
  • All transcripts of the years / semesters validated since the high school diploma at the date of application.

  • Certificate of English level (compulsory for non-English speakers).

    (Proof of English level B2 or equivalent)
  • Curriculum Vitae.

Additional supporting documents
  • VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).

  • Supporting documents :
    - Residence permit stating the country of residence of the first country
    - Or receipt of request stating the country of first asylum
    - Or document from the UNHCR granting refugee status
    - Or receipt of refugee status request delivered in France
    - Or residence permit stating the refugee status delivered in France
    - Or document stating subsidiary protection in France or abroad
    - Or document stating temporary protection in France or abroad.

Contact(s)
Course manager(s)
Admission