PREparatory classes, 2.5 ECTS each. Mandatory for all M1 [AI] students, except PRE3.
Prerequisites for M2 students who want to follow M2 [AI] classes.
If you cannot take them, study on your own online CRASH COURSES.

• • PRE1: APPLIED STATISTICS - online class, website. - Statistiques appliquées

  • PRE2: MATHEMATICS FOR DATA SCIENCE  (details) -online-class, website- Mathématiques pour les sciences des données 

  • PRE3: RELATIONAL DATABASES  ecampus page (details from last year) - Bases de données relationnelles, SQL

  • PRE4: SCIENTIFIC PROGRAMMING (syllabus) -online-class, website- Programmation scientifique en Python

Tronc commun (TC) “classic classes", 2,5 ECTS each. 

To get a consistent path of study with adequate prerequisites, among optional classes, always take either green (ML path) or brown (NLP path) classes (or both).

• • TC0: Foundational Principles of Machine Learning (FPML, organization, all material here) - [ecampus: TBA].- Introduction to Machine Learning -- with PRE1 and PRE2 as prerequisites, and PRE4 is strongly recommended. Note that PRE1 and PRE2 are mandatory, i.e., you must attend them to be allowed to follow TC0/FPML, except if you can argue that you are already very fluent in statistics (PRE1) and linear algebra (PRE2). This class (or an equivalent) is a prerequisite for almost all other [AI] classes. 

  • TC1: Machine Learning Algorithms - [external site][ecampus]. - Algorithmes d'apprentissage -- with TC0 as prerequisite. This class is a prerequisite for OPT4 (DL).

  • TC2: OPTIMIZATION -- website 2021/2022 - Optimisation, descente de gradient, etc. -- with PRE2 as prerequisite

  • TC3: INFORMATION RETRIEVAL (website from previous year)-[ecampus].- Recherche et extraction d’information dans les textes  -- with PRE1, 2, and OPT17 "Hands-on NLP" as prerequisite.

  • TC6: Large-Scale Distributed Data Processing (website from last year) -- Algorithmes distribués et bases de données -- with PRE3 as prerequisite (or good knowledge of database knowledge systems, at least of SQL).

Formerly all OPTional classes, though some in bold, are now mandatory for the [AI] track :-) 2.5 ECTS each. 

• • OPT4: DEEP LEARNING -- With TC1 as prerequisite. Highly recommended for all, mandatory for all [AI] students.

  • OPT8: HISTORY OF AI [ecampus]

  • OPT9: Hands-on Machine Learning (details)[external site][ecampus]. participate to a challenge -- With PRE1 and PRE2 as prerequisite.

  • OPT13: INFORMATION THEORY (website from a previous similar class). Théorie de l'information -- With PRE1 as prerequisite

  • OPT 15: Fairness in AI: New class. Syllabus. With PRE1 as prerequisites. Recommended to be taken jointly with OPT 16.

  • OPT 16: Creation of a challenge in Artificial Intelligence  (Website from previous years) [ecampus].: Create a challenge (that other students will solve as a TER project). Team work in teams of 5-6 people. -- With TC0 and PRE4 (or equivalent) as prerequisite.

  • OPT 17: Hands-on Natural Language Processing [external site]. New class to learn how to manipulate large text corpora.

• • Summer school (école thématique)

  • Internship (stage)

  • TER: Travail d'Etude et de Recherche (small internship). Personal work supervised by a member of the master's teaching team or in s university research lab. This work can take the form of a state of the art on a given scientific subject and / or the implementation of state algorithms for application on a given problem. This work is normally not remunerated However, If an internship is longer than 2 months it must be remunerated.

• • Languages: French or English for non-native speakers.

  • I&E: Innovation and Entrepreneurship.

  • FVE: Research and Development training, mutualised with avec MIAGE d'Orsay. Formation à la Vie en Entreprise

  • Teacher responsible for I&E basics/BDlabs (M1 EIT) is: Guillaume DION

Tronc commun (TC) “classic classes", mandatory for all [AI] students, 2,5 ECTS each. 

• • TC4: PROBABILISTIC GENERATIVE MODELS (website from last year) (NEW website 2020/2021)-online-class, website- HMMs etc. -- with PRE1 and 2 as prerequisite

  • TC5: SIGNAL PROCESSING (Website from last year) -online-class, website- Traitement du signal -- with PRE2 and 4 as prerequisite

Formerly all OPTional classes, though some are now mandatory :-) 2.5 ECTS each. 

green (ML path) indicates Machine Learning of Computer Vision classes or brown (NLP path) indicate Natural Language Processing classes. 

OPT1: GRAPHICAL MODELS (details) (website from previous year) [ecampus]. Modèles graphiques pour l’accès à l'information à grande échelle -- With TC4 as prerequisite

OPT2: COMPUTER VISION (website from last year) New class, syllabus -- With TC1 and OPT4 as prerequisite.

OPT3: REINFORCEMENT LEARNING (overleaf)  [OLD external site][ecampus]. Apprentissage par renforcement -- With TC1 as prerequisite.

OPT5: AUTOMATIC SPEECH RECOGNITION AND NATURAL LANGUAGE PROCESSING [external site][ecampus].

OPT6: LEARNING THEORY AND ADVANCED MACHINE LEARNING   [external site] [ecampus]. Apprentissage avancé et théorie -- With TC1 as prerequisite

OPT7: ADVANCED OPTIMIZATION AND AUTOMATED MACHINE LEARNING  [external site][ecampus] (Formely Optimisation avancée,) -- With TC2 as prerequisite

OPT 10: IMAGE MINING -- With PRE1, 2, and 4, as prerequisites.

OPT 11: DEEP LEARNING FOR NLP  (website from previous year)   [external site][ecampus]. Natural Language Processing -- With OPT4 as prerequisite.

OPT 12: TEXT MINING AND CHATBOTS -- [external site][ecampus]- With TC3 and 6 as prerequisite.

OPT 14: MULTILINGUAL NATURAL LANGUAGE PROCESSING (details) [external site][ecampus] -- With TC4 as prerequisite.

• Communitation

• I&E: Innovation and Entrepreneurship.

• Conferences

• Teacher responsible for I&E Study (M2 EIT ) is: Alvaro PINA STRANGER

5 to 6 month internship in a research lab or a company (Coordination: Marc Evrard and Thomas Gerald).