This document is susceptible to slightly evolve for the year 2021-2022!
ProgramAll Data AI courses are 24h, count 2.5 ECTS, and are validated by labs, presentations and/or exams. A course is validated when one obtain an average of, at least, 10/20.
M1 yearTo validate the M1 year, a student must accomplish the following:
- validate two research projects, each awarded 5 ECTS
- acquire a total of at least 50 ECTS in courses with at least 40 courses in Data AI courses
M2 yearTo validate the M2 year, a student must accomplish the following:
- fulfill all the Data AI mandatory requirements (see below)
- acquire a total of at least 30 ECTS courses (including the mandatory courses) with at least 25 ECTS in Data AI courses
- validate the M2 internship for 30 ECTS
Data AI mandatory requirementsStudents must validate at least one course for each of the following groups, before the end of the M2 year:
- Group Machine Learning:
- TPT-DATAAI902 - Machine Learning: Shallow & Deep Learning (Mounim El Yacoubi)
- X-INF554 - Machine & Deep Learning Introduction (M. Vazirgiannis)
- TPT-DATAAI901 - Machine Learning (Filippo Miatto)
- Group Logics:
- TPT-IA301 - Logics and Symbolic AI (Isabelle Bloch & Natalia Diaz)
- TPT-SD206 - Logic & Knowledge representation (J.-L. Dessalles)
- Group Big Data Systems:
- X-INF583 - Systems for Big Data (Angelos Anadiotis / Yanlei Diao)
- TPT-DATAAI921 - Architectures for Big Data (Ioana Manolescu)
- TSP-CSC5003-1 - Big data infrastructures (Bruno Defude)
- TPT-DATAAI922 - Big Data Processing (Louis Jachiet)
- Group Databases:
- X-INF553 - Database management systems (Ioana Manolescu)
- TPT-SD202 - Databases (Maroua Bahri)
- Group Softskills:
- TPT-DATAAI941 Softskills seminar - Softskills seminar (M2 only) (Fabian Suchanek)
- Group Ethics:
- TPT-DATAAI951 - AI Ethics (Maxwell Winston, Sophie Chabridon, Ada Diaconescu, Fabian Suchanek)
Research projects (mandatory for M1 students)
ContextResearch projects are a way for students to have a first contact with research, they correspond to a short internship of roughly 10 days worth of work but scattered throughout a semester. The goal of each research project should be to make a contribution to research in the broad sense, which includes re-implementing well known techniques, benchmarking different approaches, etc. Research projects are for M1 students. M1 students have to validate two research projects. Theses two projects can be with the same adviser but the second project will be approved only after the first one is finished.
Selecting a topicStudents are encouraged to propose topic and find someone to advise them but the Data AI program will provide a list of research projects topics. In both cases, the student should find a research project adviser. Once the student and the adviser both agree on making a research project together and on a topic, the advisor should send an email to the master team containing the name of the student, the project description. Once the research project has been approved by the Data AI team, it can start.
Doing the research projectThe student should spend 10 days in a lab working with his/her adviser on the selected research topic. The work of students can include reading articles, writing code or ideas, making experiments, etc. At the end of the research project the student should write a short document summarizing its contribution.
After the research projectAt the end of the year, the Data AI team will organize a "poster session". Each student should prepare a poster that will be printed by the Data AI team. M1 students need to validate two research projects, the poster can dedicated to one or both projects.
Timeline for 2020 S1 research projectsStudents should have a research project by Nov 1. (this means finding an advisor and having him send an email to the Data AI team by this date). The project represents 10 full days of work, typically spread out at a pace of about one day per week. First semester projects must be completed by February 3, 2021 (note that, for the year 2020-2021 M1 students can do their two research projects in semester 2). Keep in mind that there are two weeks of break over Christmas, so you need to start the project by Nov. 9 at the latest.
Time frameThe master’s M2 program includes an internship of at least 4 months and at most 6 months. The internship should take place in the second half of the study year. The internship should be completed during that study year. This means that the internship should start in the month following the exams of the first period (around February) and by 1st of June at the latest, so as to be completed by the 1st of October.
Finding an internshipThe internship can take place either in a company or in a research lab, in France or abroad. Students who aim to do a PhD are encouraged to do their internship in a research lab, so that the internship could potentially lead to a PhD. The student is responsible for finding an internship. The program coordinators (Goran Frehse and Louis Jachiet) will send internship offers via the mailing list. The reference institute (Télécom Paris) usually also organizes internship forums. Students who wish to do an internship in a research lab are also encouraged to contact the lecturers directly to see if internship positions are available.
The Internship Agreement
- The student makes sure she/he has a proper internship offer. The offer should be a document that contains at least the company name and address, the location and duration of the internship, the required skills/qualifications of the student, the title of the internship, and at least one paragraph about the expected work. The document should be in English or French (translations provided by the student are OK, if accompanied by the original document).
- The student makes sure that the topic of the internship falls broadly into the thematic scope of the master's program, and that the internship will be primarily on a single project (rather than helping out with different small tasks). If in doubt, the student checks with the program coordinators before applying to the internship.
- The student applies to the internship, and, when accepted, proceeds with the following steps.
- The student fills out the form in the Synapse system. All tabs should be filled. If the form does not appear in Synapse, please contact Danielle (and put the program coordinators in Cc).
- The student sends the internship offer (see Point 1) by email to the program coordinators, so that they can propose a scientific advisor.
- The student finds an scientific advisor (generally the one proposed by the program coordinators) which is a lecturer of the Data AI program from IP Paris, who is different from the advisor of the internship. If the lecturer agrees to be the scientific advisor, the student informs the internship coordinator (currently Danielle Deloy) of the choice. This shall happen via an email that includes the name, the email address, the phone number, the professional address, and the institute of the scientific advisor, with the advisor in CC.
- The host institution of the internship designates an internship advisor, who supervises the student during the internship. This person is typically an employee of the company where the internship takes place, or the researcher with whom the student wants to work in case of a research internship.
- The student fills out the internship agreement (a form with the title “CONVENTION DE STAGE”). The “Etablissement d’inscription administrative” is the university in which the student is registered (e.g., Paris-Sud or Télécom Paris). The “Organisme d’accueil” is the host institution. The “Stagiaire” is the student. The “enseignant référent” is the scientific advisor. The “tuteur” is the internship advisor. The program coordinators do not appear in this agreement.
- The student prints 6 (in words: six) copies of the internship agreement, and brings them to the internship coordinator (currently Marion Turgis, Office B650 at Télécom Paris) for signing.