M2 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 from examples.

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 big data, image and language processing.

This second year offers an expanded choice of options, covering ethical aspects and other topics such as starting a company.

Location
ORSAY
GIF SUR YVETTE
Course Prerequisites

This course requires a good background in mathematics and computer science: - Probability and statistics - Linear algebra - Differential and integral calculus - Scientific programming - Visualization of the data Applicants should also have completed successfully the M1 of Artificial Intelligence (or equivalent): - Know the basics of applied statistics and optimization - Know how to manipulate big data - Know how to differentiate and apply techniques of supervised, unsupervised, and reinforcement learning - Know how to program predictive models with Python and master scikit-learn - Know how to to visualize data and illustrate results with programming tools - Know how to write a project proposal and communicate results in writing and orally

Skills
  • Mathematically formulate gradient descent algorithms for deep neural networks, graphical models, or other statistical learning models.

  • Program deep learning models and graphical models using Python and acquire proficiency in Keras, TensorFlow and/or Pytorch.

  • Understand the foundations of statistical learning at theoretical level, focussing on over-learning and regularisation.

  • Analyse data of various types (image, text, speech) from the raw signal.

  • Read, summarise, comment on and reproduce scientific articles.

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.

Collaboration(s)
Laboratories

Laboratoire de recherche en informatique
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
Laboratoire Spécification et Vérification
Laboratoire des Signaux et Systèmes.

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).

Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
TER Stage 10
Stage long 30
French Language and Culture 2 2 21
French Language and Culture 1 2 30
EIT - Summer School 4
EIT - Innovation and Entrepreneurship Basics 2 3
EIT - Innovation and Entrepreneurship Basics 1 3
EIT - Innovation & Entrepreneurship Study 2 3
EIT - Innovation & Entrepreneurship Study 1 3 21
EIT - Innovation & Entrepreneurship Advanced 2 2.5
EIT - Innovation & Entrepreneurship Advanced 1 2.5 21
EIT - Business Development Lab 2 5
EIT - Business Development Lab 1 4
[SOFT] Soft skills - Transversal Project B 2.5 7 7 7
[SOFT] Soft skills - Transversal Project A 2.5 7 7 7
[SOFT] Soft skills - Summer school 2.5 21
[SOFT] Soft skills - Seminars B 2.5
[SOFT] Soft skills - Seminars (Fairness in Data Science) 2.5 20
[SOFT] Soft skills - 5 Innovation et Entreprenariat avancé 2.5 21
[SOFT] Soft skills - 4 Innovation et Entreprenariat 2.5 21
[SOFT] Soft skills - 3 (Formation à la vie de l'entreprise - Initiation) 2.5 21
[SOFT] Soft skills - 2 (Communication) 2.5 21
[SOFT] Soft skills - 1B (Langue) 2.5 100
[SOFT] Soft skills - 1A (Langue) 2.5 21
[PDCS] Programmation orientée objet 2.5 11 10
[PDCS] Programmation GPU 2.5 12 9
[PDCS] Programmation avancée C++ 2.5 9 0 12
[PDCS] Ordonnancement et systèmes d'exécution 2.5 21
[PDCS] Optimisation stochastique 2.5 21
[PDCS] Modélisation et optimisation des systèmes discrets 2.5 21
[PDCS] Jeux, apprentissage et optimisation des systèmes complexes 2.5 21
[PDCS] Initiation au calcul quantique 2.5 21
[PDCS] Frontières du calcul parallèle et distribué 2.5 21
[PDCS] Calcul Haute Performance 2.5 12 9
[PDCS] Big Data 2.5 12 3 8
[PDCS] Auto-stabilisation 2.5 21
[PDCS] Algorithmique parallèle 2.5 12 6 3
[PDCS] Algorithmes distribués robustes 2.5 21
[PDCS] Algorithmes de la nature 2.5 21
[ISD] Traitement distribué des données. 3 25
[ISD] Traitement automatique des langues 3 25
[ISD] Test et Vérification 3 25
[ISD] Services et applications Web 3 25
[ISD] sécurité 3 25
[ISD] Réseaux sans fil 3 25
[ISD] Réseaux 3 25
[ISD] Représentation des connaissances et visualisation 3 25
[ISD] Rapport d'activité 6 5
[ISD] Projets tuteurés 6 25
[ISD] Projet étude de cas 3 25
[ISD] Programmation système et réseau 3 25
[ISD] Probabilités/Statistiques 3 25
[ISD] Politiques et concepts avancés en sécurité 3 25
[ISD] outils pour la manipulation et l'extraction de données 3 25
[ISD] Optimisation 3 25
[ISD] Modélisation 3 25
[ISD] Modèles Mathématiques 3 25
[ISD] Mémoire 12 8
[ISD] Machine learning/Deep learning 3 25
[ISD] langages Dynamiques 3 25
[ISD] IoT (Internet des objets) 3 25
[ISD] Introduction à l'apprentissage 3 25
[ISD] Extraction et programmation statistique de l'information 3 25
[ISD] Droit informatique 3 25
[ISD] Data Warehouse II 3 25
[ISD] Data Warehouse I 3 25
[ISD] Data Lake 3 25
[ISD] Communication 3 25
[ISD] Cloud Computing 3 25
[ISD] Blockchain 3 25
[ISD] Anglais 3 25
[ISD] Anglais 3 25
[ISD] Algorithmique distribuée 3 25
[ISD] Algorithmique avancée 3 25
[HCI] Virtual Humans : Project 2.5 21
[HCI] Virtual Humans 2.5 21
[HCI] Studio Art Science 2.5 21
[HCI] Serious games : project 2.5
[HCI] Serious games 2.5
[HCI] Programming of Interactive Systems 2 2.5
[HCI] Programming of Interactive Systems 1 2.5
[HCI] Mixed Reality and Tangible Interaction - Project 2.5 21
[HCI] Mixed Reality and Tangible Interaction 2.5 21
[HCI] Interactive Machine Learning : Project 2.5
[HCI] Interactive Machine Learning 2.5
[HCI] Interactive Information Visualization : Project 2.5
[HCI] Interactive Information Visualization 2.5
[HCI] Groupware and Collaborative Work : Project 2.5 21
[HCI] Groupware and Collaborative Work 2.5 21
[HCI] Gestural and Mobile Interaction 2.5
[HCI] Fundamentals of eXtended Reality 2.5
[HCI] Fundamental of situated computing 2.5
[HCI] Fundamental of Human-Computer Interaction 2 2.5
[HCI] Fundamental of Human-Computer Interaction 1 2.5
[HCI] Experimental Design and Analysis 2.5
[HCI] Evaluation of Interactive Systems 2.5
[HCI] Digital fabrication : Project 2.5
[HCI] Digital Fabrication 2.5
[HCI] Design project - Level 2 : Project 2.5 21
[HCI] Design project - Level 2 2.5 21
[HCI] Design project - Level 1 : Project 2.5 21
[HCI] Design project - Level 1 2.5 21
[HCI] Design of Interactive Systems 2.5
[HCI] Creative Design : Project 2.5
[HCI] Creative Design 2.5
[HCI] Career Seminar - Level 2 project 2.5
[HCI] Career Seminar - Level 2 2.5
[HCI] Career Seminar - Level 1 : Project 2.5 21
[HCI] Career Seminar - Level 1 2.5
[HCI] Advanced Programming of Interactive Systems 2 2.5
[HCI] Advanced Programming of Interactive Systems 1 2.5
[HCI] Advanced Immersive Interactions - Project 2.5
[HCI] Advanced Immersive Interactions 2.5 21
[HCI] Advanced Design of Interactive Systems 2.5
[DS] Social and Graph Data Management 2.5 12 9
[DS] Semantic Web and Ontologies 2.5 12 9
[DS] Knowledge Discovery in Graph Data 2.5 12 6 3
[DS] Intelligence Artificielle, Logique et Contraintes : Projet 2.5 10.5 10.5
[DS] Intelligence Artificielle, Logique et Contraintes 2.5 10.5 10.5
[DS] Distributed Systems for Massive Data Management 2.5 12 0 9
[DS] Data Science Project 2.5 3 18
[DS] Bases de données avancées II : Transactions 2.5 9 8 4
[DS] Bases de données avancées I : Optimisation 2.5 9 8 4
[DS] Algorithms for Data Science 2.5 12 9
[ANO] Virtualisation et cloud 2.5
[ANO] Théorie des jeux 2.5 21
[ANO] Tests fonctionnels de protocoles 2.5 21
[ANO] Réseaux sans fil 2.5 21
[ANO] Réseaux mobiles 2.5 21
[ANO] Programmation système et réseaux 2.5 21
[ANO] Programmation MPI 2.5
[ANO] Optimisation multi-objectifs 2.5 21
[ANO] Optimisation discrète non linéaire 2.5 21
[ANO] Optimisation dans les graphes 2.5 21
[ANO] Internet of Things 2.5 21
[ANO] Evaluation de performances 2.5
[ANO] Blockchain 2.5
[AI] TC6: DATACOMP 2 2.5 12 9
[AI] TC5: SIGNAL PROCESSING 2.5 24
[AI] TC4: Probabilistic Generative Models 2.5 16.5 4.5
[AI] TC3: INFORMATION RETRIEVAL 2.5 9 12
[AI] TC2: OPTIMIZATION 2.5 12 4.5 4.5
[AI] TC1: MACHINE LEARNING 2.5 15 6
[AI] TC0 : Introduction to Machine Learning 2.5 15 6
[AI] PRE4: SCIENTIFIC PROGRAMMING 2.5 9 12
[AI] PRE3: DATACOMP 1 2.5 12 9
[AI] PRE2: MATHEMATICS FOR DATA SCIENCE 2.5 12 4.5 4.5
[AI] PRE1: APPLIED STATISTICS 2.5 10.5 10.5
[AI] OPT9: DATA CAMP 2.5 10 15
[AI] OPT8: GAME THEORY 2.5 12 4.5 4.5
[AI] OPT7: ADVANCED OPTIMIZATION 2.5 12 4.5 4.5
[AI] OPT6: LEARNING THEORY AND ADVANCED MACHINE LEARNING 2.5 21
[AI] OPT5 : VOICE RECOGNITION AND AUTOMATIC LANGUAGE PROCESSING 2.5 21
[AI] OPT4: DEEP LEARNING 2.5 10.5 10.5
[AI] OPT3 : REINFORCEMENT LEARNING 2.5 15 6
[AI] OPT2: IMAGE PROCESSING 2.5 21
[AI] OPT14:MULTILINGUAL NATURAL LANGUAGE PROCESSING 2.5 21
[AI] OPT1 : GRAPHICAL MODELS 2.5 15 6
[AI] OPT 13: Theorie de l'information 2.5 10.5 10.5 0 0
[AI] OPT 12: INFORMATION EXTRACTION FROM DOCUMENTS TO INTERFACES 2.5 10.5 10.5
[AI] OPT 11: DEEP LEARNING FOR NLP 2.5 18 3
[AI] OPT 10: IMAGE INDEXING AND UNDERSTANDING 2.5 15 6
Modalités de candidatures
Application period
From 01/05/2020 to 01/07/2020
Compulsory supporting documents
  • Curriculum Vitae.

  • Course selection sheet.

  • Motivation letter.

    (* Describe a personal experience that convinced you to pursue AI studies * Which classes you enjoyed in the past that 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.

  • All transcripts of the years / semesters validated since the high school diploma at the date of application.

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

Contact(s)
Course manager(s)
Aurelien DECELLE - aurelien.decelle@lri.fr
Isabelle GUYON - isabelle.guyon@lri.fr