Teaching‎ > ‎

Université Paris-Saclay CS master in Artificial Intelligence (previously AIC)

 [1st year (M1 UPSaclay)]       [2nd year (M2 UPSaclay)]        [EIT DSC master in Data Science] 

AI master program

Frequently Asked Questions

Isabelle Guyon & Aurélien Decelle, responsible of the AI master program
Alexandre Verrechia (UPsaclay) & Aurélie Lerasle (EIT digital), administrative assistants
Enrollment open. Deadline June 30, 2020
For scholarship applicants, the deadline in May 15, 2020.

Question #1: I submitted an application xxx time ago and am without news. When will decisions be rendered?

The admission process this year is handled at the department level. The jury will meet in July and decisions will hopefully be sent before the end of July. The delays can be partially imputed to the fact that we received hundreds of applications, the Covid crisis, and the mutations undergone by University Paris-Saclay. Thank you for your patience.

Contents:



Is this master for me?

Is this a one-year or a two-year program?
Two years: M1 and M2.

What is the entry level?
Bachelor in Computer Science, Statistics or Math (3 or 4 years) or French L3 completed (licence en informatique ou mathématique+informatique).

Are courses taught in English or French?
The language of instruction will be English but the instructors are (mostly) French, so you should be ready to get sometimes relatively “broken English”. Also, some of the slides or teaching material might not be translated (yet).

What skill will I learn?
- Applied statistics and optimization.
- Handling big data.
- Supervised, unsupervised, and reinforcement learning, theory and practice.
- Predictive modeling and data visualization with Python, scikit-learn, Pytorch, Tensorflow.
- Cutting edge AI modeling techniques, including Deep Learning.
- Applications of machine learning to computer vision, voice recognition, text processing, natural language processing.
- Writing project proposals and communicating results in writing and orally.

See the provisional course list. Each module is a "teaching unit", in French Unité d'Enseignement or UE], worth 2.5 ECTS

How many ECTS do I earn from this program?
120 ECTS (60 each year). In total (see preliminary directives), you will have to earn:
- 70 ECTS by taking classes from the AI program, 
- 20 ECTS by taking classes common to all, or borrowed from other master programs at UPSaclay ((languages, communication, entrepreneurship, research and development, conferences or seminars, etc.)
- 30 ECTS from the last semester 5-6 month internship.

What careers does this open to me?
Students who have followed this program will have solid theoretical and practical training. Projects and internships prepare them to enter the workforce. This master's degree opens towards research and teaching, as well as to careers in the industrial sector or services. Some of our graduates 
end up working in startups or integrate R&D departments of companies such as Thales, Orange, HP, IBM Research, Yahoo, Google, Facebook.

Can I apply if I am NOT a Computer Science (CS) major?
Yes, but you should meet the prerequisites. CS, Math, Statistics, or Physics majors are probably best prepared.

What are the prerequisites?
Good bases in mathematics and computer science: 
- Probability and statistics 
- Linear algebra 
- Differential and integral calculus 
- Scientific programming 
- Data visualization

Will there be entry-level M1 classes, to help me fill some of my "missing prerequisites"?
There are refresher classes (the "PRE" modules, and TC0), but not sufficient for students who have not taken such classes at all as undergraduates. Your grades in MATH in particular will be an important criterion of selection.
- PRE1: Applied statistics
- PRE2: Mathematics for data science
- PRE3: Datacomp
- PRE4: Scientific programming
- TC0: Machine learning 1

Where is UPSaclay, is it in Paris?
No, it is in the outskirts of Paris, about 1 hour away by public transportation.
For people who like outdoors, hiking, sports, it is really nice. You can get to Paris easily and enjoy theater, restaurants, monuments, and museums. Locally, there are arts-and-craft, theater, sports, and other clubs, and limited local shows and exhibitions.

Do students have the possibility of starting a Ph.D. thesis straight after graduating from M2?

In France, to start a PhD, you need to find an advisor first. To get a scholarship, you need to write a proposal and get letters of support. However, there are some advisors that already have funding and advertise for an open PhD position.

Should I apply for UPSaclay or EIT DSC?
It depends. The UPSaclay degree is a 2-year degree at UPSaclay (in France). The EIT DSC degree is a European degree in which you do 1-year in one university and the other in another one. You would do one of the two years at UPSaclay, for instance, and the other elsewhere in Europe. The classes are mostly the same, but the EIT program has extra "entrepreneurship" training.

Can I apply to UPSaclay even though I am an international student and the AI master in not marked [here] as an "international master"?
Yes!

What are the tuition fees?
The differ for UPSaclay and EIT Digital. The UPSaclay degree is relatively inexpensive, compared to EIT fees . There is an extra FEE for non EU citizens.

COVID-19 crisis: What will happen to the selected students if the 2020-2021 academic year begins before the borders open?
All courses will be available to follow remotely.



Can I apply and how?

Where and when can I apply?
- [HERE]  for UPSaclay (application period from 01/05/2020 to 01/07/2020) -- download instructions. Contact support.
- [HERE] for EIT DSC (applications for 2020 have ended).

Can I apply to multiple masters or parcourses?
Yes

What will I need to provide with my application?
- Curriculum Vitae (in any format)
- Letter of motivation:
    * 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
- Certified transcripts of grades for all diplomas since high-school.
- "Fiche de choix" (selection sheet), see below.
- Dossier VAPP (optional, see below).
- Recommandation letter or internship evaluation (recommended).

Note: for international students, you are asked to provide 2 contact persons to serve as reference to recommend you. This is redundant with "Recommandation letter or internship evaluation". You can either upload the recommendation letter manually (yourself) in this section, or skip it. Your references will be contacted anyways separately to provide a recommendation.

Should the application be in French or in English?
Either way.

I submitted my candidature but forgot something, how can I add it?
You cannot, sorry. You must destroy your first candidature and create a new one!

I destroyed my application accidentally can you revert this or accept my files by email?
No. You need to create a new account and re-enter everything on the platform by hand.

Due to special circumstances (e.g. COVID-19 epidemic) I had to telecommute for my internship, should I mention it?
You can add internships performed while telecommuting in your work experience, as well as other work experiences performed during the confinement, as long as you could perform your duties.

Where is the "Selection Sheet" ("Fiche de choix"), and what is this good for?
It is "somewhere: on the website, but if you cannot locate it:
Use it to rank order CS mater specializations by preference. Or use this:

Is the "M1 machine learning" the same thing as "M1 artificial intelligence"?
Yes, there seems to be an incoherence on the platform. If you see that, it is OK.

Can I enter directly in the second year (M2 level)?
We do not recommend it, unless you have completed a 4-year Computer Science Bachelor degree or equivalent, with a strong emphasis on Machine Learning/AI/Data science. The classes you should have taken should include most of the M1 classes of our program, and at least all PRE classes, TC0-3, and OPT4. In exceptional cases, you may also apply to enter at the M2 level if you interrupted your studies more than 2 years can justify of continuous education classes and/or professional experience that is equivalent to the M1 classes we offer. In this case, you should prepare a special application (VAPP=Validation des Acquis Professionnels et Personnels).

What are the conditions to attend the M2 level if I am a third-year student of an engineering school (double cursus)?
You may apply, but take into account the prerequisites. You may have to follow M1 level modules, e.g. 2 PRE classes to catch up and be allowed to follow more advanced classed. At the M2 level, two PRE classes count for only one module (2.5 ECTS). Check with your school the number of ECTS you need to graduate. As of last year, the rules were:
- The ENSIIE students must follow at least 10 modules instead of 12 in S1 (the 6 TC and 4 OPTs in theory). The students earn 5 ECTS of the ENSIIE. If they take more modules in the M2 program, only the 10 best results count in the average. In S2 they are exempt from FVE and English, unless they wish.
- The ENSTA students must follow at least 7 modules instead of 12 in S1 (3 TC and 4 OPTs in theory). The students earn 12.5 ECTS from ENSTA. If they follow more modules in the M2 program ,only the 7 best results count in the average. In S2 they are exempt from FVE and English, unless they wish.
- The CentraleSupélec students must follow at least 4 modules instead of 12 in S1 (2 TC and 2 OPTs in theory). The students earn 20 ECTS from CentraleSupélec. If they follow more modules in the M2 program, only the 4 best results count in the average. In S2 they are also exempt from FVE and English, unless they wish.
- The Télécom students must follow the M2 curriculum in S1, similarly to the "classic" M2 IA cursus. In S2 they are exempt from FVE and English, unless they wish.

Do I need to include a "Dossier VAPP"?
Not necessarily. Only if you cannot justify of the right academic profile and you want other personal or professional experiences to be taken into account. See VAPP instructions. This Dossier VAPP can be useful at the M1 or M1 entry level, if you interrupted your studies more than 2 years, and want to capitalize on personal or professional experience to compensate for missing academic credentials.  

As a foreign student, I cannot see my diplomas in the list, what should I select?
- Baccalauréat ou équivalent:
    * Equivalence BAC diplôme ou titre étrang.
    Equivalence BAC
- Cursus précédent:
    * Bachelor of Science

Do I need to submit my high-school grade transcript?
No.

I am a foreign student, do I need to complete the Campus France Procedure (CEP) and when?
Yes, if you come from one of the following countries:
Algérie, Argentine, Bénin, Brésil, Burkina Faso, Burundi, Cameroun, Chili, Chine, Colombie, Comores, Congo, Corée du Sud, Côte d'Ivoire, Djibouti, Egypte, Etats-Unis, Gabon, Guinée, Haïti, Inde, Indonésie, Iran, Japon, Koweit, Liban, Madagascar, Mali, Maroc, Maurice, Mauritanie, Mexique, Nigeria, Pérou, République démocratique du Congo, Russie, Sénégal, Singapour, Taïwan, Tchad, Togo, Tunisie, Turquie, Vietnam.
As soon as you are admitted, you must go through the procedure, if you are not already studying in France and need a VISA. These FAQ should help you.

Can I get a scholarship to help me pay for my studies?

- If you applied to the EIT program, you may be selected for a merit-based scholarship by the EIT program, depending on your rank in the application review process. EIT grants cannot be cumulated with Paris-Saclay grants or Campus-France grants.
- There are several merit-based scholarships from Paris-Saclay and Campus-France (see below). We must submit the request on your behalf.
- For Paris-Saclay grants, you must submit your application to the master program first!
- Do not wait for the application deadline of June 30 BECAUSE WE MUST REVIEW AND SELECT CANDIDATES before MAY 21.
- Please submit your application no later than MAY 15, 2020 and contact Isabelle Guyon by email to request a scholarship.
- If you miss the deadline of May 15, but it is BEFORE May 31st, contact us, in case we don't have many candidates for the Digicosme scholarship.

    1) University Paris-Saclay international scholarships:
- If you are an international student, you may be contacted to receive a merit-based grant of IDEX Program "Investissement d’Avenir 3" [details][cadrage April 11].
- There are one or two-year scholarships of 10 000 € per student per year. 
- Candidates selected for this grant will receive an email and instructions how to apply (they cannot apply directly). 
- A prerequisite is to have been accepter to the master and pre-selected, hence please submit your application to the master program first  and contact us by MAY 15, 2020. 
- Your deadline for submission (once you are pre-selected) is MAY 25, 2020 (recommendation letters submitted should be received before MAY 27).
- Evaluation criteria:
    * Academic level
    * Personal project
    * Motivation to pursue a PhD thesis (for M2 students).

    2) Labex Digicosme scholarships:

- There are only three Digicosme grants for our program of 12 000 € per student per year. 
- Submit a request letter to Isabelle Guyon, by MAY 15, 2020,  including 
    * a copy of your ID (passport or identity card)
    * Family name
    * First name:
    * Email: 
    * Phone (please specify in which time zone you are and what is a convenient time to contact you):
    * Gender: 
    * Citizenship:
    * Current university/school:
    * Highest degree and major (completed by start of this master degree):
    * Date of highest degree (or anticipated date of completion):
    * Spoken/written French level: None/Beginner/Good/Fluent
    * Spoken/written English level: None/Beginner/Good/Fluent
    * The name and contact information (email and phone) of two references.
- For the rest of the information, we will use your application. So, please submit your application to the master program first!
- This is a merit-based grant, but in case there are many applicants, please also justify of your needs is your request letter. 

Exceptional candidates will be proposed for receiving a grant to the Labex Digicosme jury by Isabelle Guyon and Aurélien Decelle. They will add to the application of the student a reasoned opinion justifying the excellence of the student's training in relation to the selected AI master program and themes of the Labex DigiCosme. Criteria will include the quality of the institution of origin, recommendations, and direct discussions with the candidate. An important element will be the potential to pursue a PhD thesis.

    3) Eiffel international scholarships:
The Eiffel Program is reserved for non-French nationals. Candidates with two nationalities, one of which is French, are not eligible.
Candidates must be 30 or under.
- Candidates must submit their application to the persons responsible for the master, NOT to the Eiffel program directly.
The Eiffel scholarship consists of a monthly allowance of €1,181 (a maintenance allowance of €1,031 plus a stipend of €150) and other benefits. It can be obtained for one or two years.
For this year the deadline is past (was in january 2020). Keep this in mind for 2021/2022 [see instructions from Université Paris-Saclay]. Contact: Mme. Julie HERISSON.

    4) Charpak international scholarships:
- The Chapak program is reserved to Indian nationals.
- It includes a living allowance of 700 euros per month, a tuition waiver, and other benefits.
- It is conditioned on attaching a letter of acceptance to our master, which we cannot deliver until July or so, for first time entry students. So this will concern mainly M2 students, presumably.
For this year the deadline is past (was in april 2020). Keep this in mind for 2021/2022.

    5) Other scholarships

As an M1 student, can I get a grant to study abroad?
Yes, this is called a "bourse de mobilité sortante". For this year the deadline is past (was in march 2020). Keep this in mind for 2021/2022.

When will I know whether I am accepted?
Within 2 months of depositing your files (validated and submitted by you).

What are the admission criteria?
The jury will consider all applications and rank them according to: reputation of undergraduate degree, grades, pre-requisite classes taken, past job experiences, recommendations, and motivation. Everything counts.

How many students are admitted?
- In M1: 15 to 20 students, EIT included. 
In M2: 20 to 25 students, EIT included. 



How are classes organized?

From 2020-2021, there will now be a global schedule system between Masters at Université Paris-Saclay, with semesters divided into 2 equivalent periods:
 T1 / T5 : Sept. 7 to Oct. 23 
 T2 / T6 : Nov. 2 to Dec. 18
 T3 / T7 : Jan. 4 to Feb. 19
 T4: Mar. 1 to Apr. 16
See yearly schedule or EDT (Emploi Du Temps). Students can select UEs in other Masters of the mention "informatics". For the IA Master, it is planned that the courses will take place on Thursday and Friday with approximately 4 courses per period. Each course takes 1/2 day (3.5 hours) and courses last 7 weeks (including 1 exam week, if there is a final exam). Each UE will be worth 2.5 ECTS (credits). Wednesdays will be reserved for "soft skills" such as communication, conferences, language courses ...

M1 (60 ECTS) M2 (60 ECTS)
S1 (30 ECTS) S2 (30 ECTS) S3 (30 ECTS) S4 (30 ECTS)
T1  (15 ECTS) T2 (15 ECTS) T3 (15 ECTS)  T4 (15 ECTS) T5 (10 ECTS) T6 (10 ECTS) T7 (10 ECTS) T8 (30 ECTS)
4 UE disciplinaires (au moins 2 obligatoires): PRE1, PRE2, PRE3, PRE4 4 UE disciplinaires (au moins 2 obligatoires): TC0, TC2, OPT8, OPT9 4 UE disciplinaires (au moins 2 obligatoires): TC1, TC3,  Projet A, DS Distributed systems   4 UE disciplinaires (au moins 2 obligatoires): TC6 , OPT4, OPT13, Projet B, HCI interactive ML 3 UE disciplinaires (au moins 2 obligatoires) TC4, TC5, OPT2, OP10 3 UE disciplinaires (au moins 2 obligatoires) 4 parmi: OPT1, OPT3, OPT5, OPT11 3 UE disciplinaires (au moins 2 obligatoires): OPT6, OPT7, OPT12, OPT14 Stage
1 UE d'ouverture (anglais/français) 1 UE d'ouverture (anglais/français TOEIC) 1 UE d'ouverture (I&E) 1 UE d'ouverture (FVE) 1 UE d'ouverture (communication) 1 UE d'ouverture (conferences) 1 UE d'ouverture (I&E)
Ecole thématique/Stage (5 ECTS) TER (5 ECTS)  

14 UE (Unité d'Enseignement = course) over 2 years are necessary to validate a full parcourse.
Boldface UE are mandarory for both ML and NLP parcourses
In boldface green: mandatory for ML (Machine Learning) sub-parcourse [can also be taken as an option]
In boldface brown: mandatory for NLP (Natural Language Processing) sub-parcourse [can also be taken as an option]

The students are expected to validate 120 ECTS over 2 years:

- In M1 (60 ECTS): 
  • Bloc 1: 20 ECTS through 8 mandatory UEs for the AI Master (including a project).
  • Bloc 2: 20 ECTS through 8 optional UEs, which may include AI Master UEs or other informatics UEs.
  • Bloc 3: 20 ECTS through "soft skills" UEs (such as languages; also called UE d'ouverture) and a TER internship

- In M2: 
  • Bloc 1: 15 ECTS through 6 mandatory AI Master UEs
  • Bloc 2: 7.5 ECTS through 3 optional UEs, which may include AI Master UEs or other informatics UEs.
  • Bloc 2: 7.5 ECTS through "soft skills"
  • Bloc 4: 30 ECTS internship, in the second semester

WARNING: there are still a few inconsistencies because the program is new and it is very difficult to get the administrators to update [this site]. Please bear with us.



What courses can I choose from?

You must take some mandatory classes and choose from optional classes. Some classes from other parcourses may also be available, see classes underlined in the provisional weekly schedule or weekly EDT.
If you do not have the right prerequisites, contact the instructors.

M1 classes, 60 ECTS

Classes in bold are mandatory. Others are optional. Among the bold classes, you may take either green (ML path) or brown (NLP path) classes in a given period.
AM = morning
PM = afternoon
ECTS = credits

 
CLASSIC BLOC 1: 8 MANDATORY CLASSES (2 per period), total 20 ECTS:
Period AI name Contents Day/Time ECTS
T1 PRE1 Applied Stat Thu AM 2.5
T1 PRE2 Math for DS Thu PM 2.5
T2 TC0 ML 1 Fri PM 2.5
T2 TC2 Optimization Thu PM 2.5
T3 Project A Project Fri PM 2.5
T3 TC1* ML 2 Fri AM 2.5
T3 TC3* Info. Retriv. Thu AM 2.5
T4 OPT4 Deep Learning Fri AM 2.5
T4 OPT13* Info. theory Thu PM 2.5
T4 TC6* Datacomp 2 Thu AM 2.5
(*) Choose the green (ML) or brown (NLP) class.

 
EIT BLOC 1: MANDATORY CLASSES, 10 UE, 25 ECTS (credits):
Period AI name Contents Day/Time ECTS
T1 PRE1 Applied Stat Thu AM 2.5
T1 PRE2 Math for DS Thu PM 2.5
T1 PRE3 Datacomp 1 Fri PM 2.5
T1 PRE4 Sci. programming Fri AM 2.5
T2 TC0 ML 1 Fri PM 2.5
T2 TC2 Optimization Thu PM 2.5
T3 TC1 ML 2 Fri PM 2.5
T3 TC3 Info. Retriv. Thu AM 2.5
T4 OPT4 Deep Learning Fri AM 2.5
T4 Project B Project Fri PM 2.5


CLASSIC BLOC 2: 8 OPTIONS (choose 2 per period), total 20 ECTS  (credits):
Options taught in English.
Other options taught in French may be available
Period AI, DS or HCI name Contents Day/Time ECTS
T1 PRE3 Datacomp 1 Fri PM 2.5
T1 PRE4 Sci. programming Fri AM 2.5
T2 OPT8 Hist. AI Fri AM 2.5
T2 OPT9 Data camp Thu AM 2.5
T3 TC1 ML2 Fri AM 2.5
T3 TC3 Info. Retriv. Thu AM 2.5
T3 DS Distrib. systems  Wed PM 2.5
T4 OPT13 Info. Theory Thu PM 2.5
T4 TC6 Datacomp 2 Thu AM 2.5
T4 Project B Project Fri PM 2.5
T4 HCI Interactive ML Fri PM 2.5

 
EIT BLOC 2: 4 OPTIONS (choose 4 from list), 
Options taught in English.
Other options taught in French may be available.
Total 10 ECTS
Period AI name Contents Day/Time ECTS
T2 OPT8 Hist. AI Fri AM 2.5
T2 OPT9 Data camp Thu AM 2.5
T3 Project A Project Fri PM 2.5
T3 DS Distrib. systems  Wed PM 2.5
T4 TC6 Datacomp 2 Thu AM 2.5
T4 OPT13 Info. Theory Thu PM 2.5

 
CLASSIC BLOC 3: SOFT courses (UE ouverture), total 20 ECTS.
Period Source Description: Day/Time ECTS
T1 FLE Anglais/Francais Mon AM 2.5
T1
Summer school   2.5
T2 FLE Anglais/Francais Mon AM 2.5
T2
Internship  2.5
T3 Mutualisé I&E Wed AM 2.5
T3
TER   2.5
T4 Mutualisé FVE Wed AM 2.5
T4
TER  2.5

 
EIT BLOC 3: SOFT courses and B&E courses, shared with EIT HCID
Period EIT HCID Description: Day/Time ECTS
T1 I&E B Innovation & Entrep. Basics Mon PM 3
T2 I&E B Innovation & Entrep. Basics Mon PM 3
T3 BDL1 Business Development Lab 1 Mon PM 4
T4 BDL2 Business Development Lab 2 Mon PM 5
T3 I&E A Innovation & Entrep. Advanced Mon AM 2.5
T4 I&E A Innovation & Entrep. Advanced Mon AM 2.5
Summer school Summer School   3
T1 & T2 French French Language and Culture Mon AM 2

M1 Artificial Intelligence class list


refresher Refresher classes:
PREparatory classes, 2.5 ECTS each. Mandatory for all M1 students. Prerequisite for M2 students who have not taken similar classes. If you cannot take them, study on your own, see: How do I get ready?

  • PRE1: APPLIED STATISTICS -- Statistiques appliquées (Marie-Anne Poursat) 
  • PRE2: MATHEMATICS FOR DATA SCIENCE -- Mathématiques pour le science des données (Anne Auger)  
  • PRE3: DATACOMP 1 -- Bases de données relationnelles, SQL (Benoit Groz) 
  • PRE4: SCIENTIFIC PROGRAMMING -- Programmation scientifique en Python (Benjamin Donnot et Laurent Cetinsoy)  

TC Foundational classes:
Tronc commun (TC) “classic classes", 2,5 ECTS each. 
Among the bold classes, you may take either green (ML path) or brown (NLP path) classes in a given period.

  • TC0: MACHINE LEARNING 1 -- Introduction au Machine Learning (Michèle Sebag, Francois Landes) -- with PRE1 and PRE2 as prerequisites.
  • TC1: MACHINE LEARNING 2 -- Algorithmes d'apprentissage (Caio Corro) -- with ML1 as prerequisite.
  • TC2: OPTIMIZATION -- Optimisation, descente de gradient, etc. (Anne Auger et Dimo Brockhoff) -- with PRE2 as prerequisite
  • TC3: INFORMATION RETRIEVAL (Kim Gerdes) -- Recherche et extraction d’information dans les textes  -- with PRE1, 2, 3 as prerequisite
  • TC6: DATACOMP 2 -- Algorithmes distribués et bases de données (Benoit Groz)  -- with PRE3 as prerequisite

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

  • OPT4: DEEP LEARNING (Caio Corro, Michèle Sebag) -- With ML2 as prerequisite
  • OPT8: HISTORY OF AI (Kim Gerdes) 
  • OPT9: DATA CAMP (Adrien Pavao) Challenges in machine learning; participate to a challenge -- With PRE1 and PRE2 as prerequisite
  • OPT13: INFORMATION THEORY. Théorie de l'information (Guillaume Charpiat) -- With PRE1 as prerequisite
  • DS: DISTRIBUTED SYSTEMS. UE from Data Science parcourse.
  • HCI: INTERACTIVE MACHINE LEARNING. UE from Human Computer Interface parcourse.

project Projects and practical experiences:
  • Summer school (école thématique)
  • Internship (stage)
  • Project A: Create a mini-challenge that other students will solve. Team work in teams of 5-6 people. (Isabelle Guyon et Kim Gerdes) -- With ML1 and PRE4 (or equivalent) as prerequisite
  • Project B: Resolve a mini-challenge. Team work in teams of 5-6 people. (Kim Gerdes)
  • TERTravail d'Etude et de Recherche. Personal work supervised by a member of the master's teaching team. 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 not remunerated.

 Other "soft" skills:
  • Languages: French or English for non native speakers (Roselyne Debrick).
  • I&E: Innovation and Entrepreneurship.
  • FVE: Research and Development training, mutualised with avec MIAGE d'Orsay. Formation à la Vie en Entreprise (Nadège Taillard, Alexandre Kaminski, Frédérique Blondel)



M2 classes, 60 ECTS

 
CLASSIC BLOC1, 6 MANDATORY CLASSES (2 per period), total 15 ECTS:
Period AI name Contents Day/Time ECTS
T5 TC5 Signal processing Fri AM 2.5
T5 TC4 Proba. Gen. Models Fri PM 2.5
T6 OPT1  Graphical models Fri AM 2.5
T6 OPT3* Reinforcement learning Thu AM 2.5
T6 OPT11* Deep Learn. for NLP Wed AM 2.5
T7 OPT7 Adv. Optimization Thu PM 2.5
T7 OPT6* Learn. Theory and adv. ML Thu AM 2.5
T7 OPT12* Text mining and chatbots Fri PM 2.5
(*) Choose the green (ML) or brown (NLP) class.
 
EIT BLOC 1: MANDATORY,  total 10 ECTS (credits):
Period AI name Contents Day/Time ECTS
T5 TC5 Signal processing Fri AM 2.5
T5 TC4 Proba. Gen. Models Fri PM 2.5
T6 OPT11 Deep Learning Wed AM 2.5
T7 OPT12 Text mining and chatbots Fri PM 2.5

 
CLASSIC BLOC 2: 3 OPTIONS (choose 1 per period),  total 7.5 ECTS:
Options taught in English.
Other options taught in French may be available
Period AI name Contents Day/Time ECTS
T5 OPT 2 Image understanding Thu PM 2.5
T5 OPT 10  Image mining Thu AM 2.5
T6 OPT11 Deep Learning for NLP Wed AM 2.5
T6 OPT3 Reinforcement learning Thu AM 2.5
T6 OPT5 Voice recognition Fri PM 2.5
T7 OPT6 Learning theory and advanced ML Thu AM 2.5
T7 OPT12 Text mining and chatbots Fri PM 2.5
T7 OPT14 Multilingual NLP Fri AM 2.5

 
EIT BLOC 2: OPTIONS (choose 4 from list),  total 10 ECTS
Period AI name Contents Day/Time ECTS
T5 OPT 2 Image understanding Thu PM 2.5
T5 OPT 10  Image mining Thu AM 2.5
T6 OPT1  Graphical models Fri AM 2.5
T6 OPT3 Reinforcement learning Thu AM 2.5
T6 OPT5 Voice recognition Fri PM 2.5
T7 OPT6 Learning theory and advanced ML Thu AM 2.5
T7 OPT7 Advanced optimization Thu PM 2.5
T7 OPT14 Multilingual NLP Fri AM 2.5

 
CLASSIC BLOC 3: SOFT courses (UE ouverture), 7.5 ECTS (credits)
Period EIT HCID Description: Day/Time ECTS
T5 Mutualisé Communication Wed PM 2.5
T6
Conférences Wed PM 2.5
T7 Mutualisé Innovation & Entrepreneurship Basics Wed PM 2.5

 
EIT BLOC 3: SOFT and B&E courses, 10 ECTS (credits)
Period EIT HCID Description: Day/Time ECTS
T5 I&E study Innovation & Entrep. Basics Mon PM 3
T6 I&E study Innovation & Entrep. Basics Mon PM 3
T5 & T6 Carreer Seminar Innovation & Entrep. Basics Fri/Thu AM 5
T1 & T2 French French Language and Culture Mon AM NA


BLOC 4: INTERNSHIP, 30 ECTS

M2 Artificial Intelligence class list


TC Foundational classes:
Tronc commun (TC) “classic classes", 2,5 ECTS each. Both are mandatory.



growth Growth classes:
Formerly all OPTional classes, though some are now mandatory :-) 2.5 ECTS each. 
Among (mandatory) bold classes, you may take either green (ML path) or brown (NLP path) classes in a given period. Green and red classes can also be taken as options.

OPT1: GRAPHICAL MODELS (Francois Yvon) Modèles graphiques pour l’accès à l'information à grande échelle -- With TC4 as prerequisite
OPT2: IMAGE UNDERSTANDING (Isabelle Bloch) Interprétation d'images -- With TC1 as prerequisite
OPT3: REINFORCEMENT LEARNING (Michele Sebag) Apprentissage par renforcement -- With TC1 as prerequisite
OPT5: VOICE RECOGNITION AND AUTOMATIC LANGUAGE PROCESSING (Kim Gerdes)
OPT6: LEARNING THEORY AND ADVANCED MACHINE LEARNING (Antoine Cornuejols) Apprentissage avancé et théorie -- With TC1 as prerequisite
OPT7: ADVANCED OPTIMIZATION (Anne Auger et Dimo Brockhoff) Optimisation avancée -- With TC2 as prerequisite
OPT 10: IMAGE MINING (Antoine Manzanera) ancien Indexation d'image et recherche par le contenu -- With TC1 and 6 as prerequisite
OPT 11: DEEP LEARNING FOR NLP (Caio Corro) Natural Language processing -- With OPT4 as prerequisite
OPT 12: TEXT MINING AND CHATBOTS (Anne Vilnat) -- With TC3 and 6 as prerequisite
OPT 14: MULTILINGUAL NATURAL LANGUAGE PROCESSING (François Yvon) -- With TC4 as prerequisite


 Other "soft" skills:
  • Communitation
  • I&E: Innovation and Entrepreneurship.
  • Conferences

project Internships:

5 to 6 month internship in a research lab or a company.



How do I get ready for the AI master program?

If you want to prepare yourself over the summer, there are several classes that we recommend, which can help you:
- At M1 entry level, prepare for striving in the program
- At M2 entry level, replace missing prerequisites:

We HIGHLY RECOMMEND at least reading the THREE CRASH COURSES highlighted in red.



Replace, review, or prepare for:

  • PRE1: Applied statistics

[CRASH COURSE] Crash Course on Basic StatisticsMarina Wahl (28 pages + questions)

[ON-LINE BOOK w. exercises] Computational and Inferential Thinking, By Ani Adhikari and John DeNero

[BOOK] Think stats, AB Downey.

[BOOK] Statistics in a Nutshell. Sarah Boslaugh and Paul Andrew Watters



  • PRE2: Mathematics for Data Science

A) LINEAR ALGEBRA

[CRASH COURSELinear Algebra Review and ReferenceZico Kolter and Chuong Do (26 pages)


[COURSE] Mathematics for Machine Learning: Linear Algebra, David Dye, Coursera (19 hours)

B) CALCULUS

[CRASH COURSE] The matrix calculus you need for deep learningT Parr, J Howard (33 pages)

Samuel J. Cooper, Coursera (19 hours)

[BOOK] Matrix Computations. Gene Golub and Charles van Loan

  • PRE3: Datacomp1


  • PRE4: Scientific programming

[COURSE] Data Analysis with PythonJoseph Santarcangelo

  • TC0 and TC1: Machine Learning


[TOTAL BEGINNER COURSE] Introduction to machine learning, Sebastian Thrun, Katie Malone, Udacity (10 lessons)

[BEGINNER COURSE] Machine Learning, Andrew Ng, Coursera (54 hours)

[BOOK] The Hundred-Page Machine Learning Book. Andriy Burkov. 


[ADVANCED COURSE] How to win a data science competition. Dmitry Ulianov, Coursera, (4 weeks); Excellent summer program!

  • TC2: Optimization
[COURSE] Introduction to optimizationby the instructors of TC2: Anne Auger and Dimo Brockhoff

  • OPT7: Advanced optimization
[COURSE] Advanced optimizationby the instructors of OPT7: Anne Auger and Dimo Brockhoff

  • OPT13: Information Theory

backoffice help --                                           bourses              easychair docs mardi 2 juin 2020 - 12h (heure de Paris) help
Ċ
Isabelle Guyon,
May 2, 2020, 1:41 PM
Ċ
Isabelle Guyon,
May 4, 2020, 2:56 PM
Ċ
Isabelle Guyon,
Jun 17, 2020, 5:58 PM
Ċ
EDT.pdf
(1757k)
Isabelle Guyon,
Jun 24, 2020, 7:37 AM
Ċ
Isabelle Guyon,
May 2, 2020, 9:23 AM
Ċ
Isabelle Guyon,
May 2, 2020, 1:41 PM
ĉ
Isabelle Guyon,
May 1, 2020, 6:14 PM
ĉ
Isabelle Guyon,
May 1, 2020, 7:29 PM
ċ
Isabelle Guyon,
May 2, 2020, 1:41 PM
ċ
Isabelle Guyon,
May 2, 2020, 1:41 PM