Program
This regulation complements the study regulation of IP Paris, as follows: Students can attend only the courses for which they registered at the beginning of the year. Students cannot register for courses whose content overlaps significantly (if in doubt, check with the program coordinators). Students cannot register for more than 65 ECTS per year. The students receive a grade for each course they are registered for, based on projects, labs, presentations, written exams, or other types of evaluations. In case a student receives a grade of less than 10/20, the course should offer one retake, which will always override the previous grade. To validate a year (M1 or M2), all of the following must apply:- the student has taken courses, internships, research projects, worth at least 60 ECTS in total
- no grade is below or equal to 7/20
- no internship grade is below 10/20
- the average grade of courses and research projects (weighted by the number of ECTS) is at least 10/20 per semester
- the specific conditions for the M1 or M2, respectively, are fulfilled (see below)
M1 year
Students can accumulate the 60 ECTS of the M1 as follows:- take Data AI courses.
- take at most two research projects awarded 5 ECTS each
- do at most one internship lasting at least 3 months for 15 ECTS
- choose up to 10 ECTS outside of DATA AI courses with at most 5 ECTS in non-math/computer science topics
Note that these constraints require you to follow a large number of Data AI courses. Note also that it can be beneficial to choose already in the M1 courses that are mandatory to validate the M2 (see below). We encourage you to do an internship.
M2 year
To validate the M2 year, a student must accomplish the following:- fulfill all the Data AI mandatory requirements (see below)
- acquire at least 25 ECTS in Data AI courses
- acquire at most 5 ECTS in a research project or non-Data AI courses
- do the M2 internship for 30 ECTS
Data AI mandatory requirements
Students must validate at least one course for each of the following groups, before the end of the M2 year:- Group Machine Learning:
- CSC_5DA01_TP - Machine Learning: Shallow & Deep Learning (Mounim El Yacoubi)
- CSC_52081_EP - Advanced Machine Learning and Autonomous Agents (Jesse Read)
- CSC_52087_EP - Advanced Deep Learning (Vicky Kalogeiton, Johannes Lutzeyer, Michalis Vazirgiannis (LIX))
- MAP_670I_TP - Machine Learning with Graphs (Jhony H. Giraldo)
- Group Logics:
- CSC_0EL07_TP - Logic, Knowledge Representation and Probabilities (Nils Holzenberger)
- APM_5AI01_TP - Logics and Symbolic AI (Isabelle Bloch)
- Group Big Data Systems:
- ECE_5DA04_TP - Big Graph Databases (Ioana Manolescu, Garima Gaur, Madhulika Mohanty (Inria))
- TSP-CSC5003 - Big Data Infrastructures & semantic networks (Julien Romero, Amel Bouzeghoub)
- CSC_52083_EP - Systems for Big Data (Oana BALALAU, Pierre Bourhis, Yanlei Diao)
- Group Databases:
- CSC_4SD02_TP - Databases (Mehwish Alam)
- CSC_51053_EP - Database management systems (Ioana Manolescu)
- Group Softskills:
- PDV_5DA05_TP - Softskills seminar (M2 only) (Fabian Suchanek)
- Group Ethics:
- HSS_5DA06_TP - AI Ethics (Maxwell Winston, Sophie Chabridon, Ada Diaconescu, Fabian Suchanek)
- Group Data AI basics:
- CSC_5DA00_TP - Data AI basics (Tiphaine Viard, Louis Jachiet, Nils Holzenberger, Jean-Louis Dessalles)
Research projects
Context
Research 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 (around 70h) 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 should not overlap with each other or with an internship.
Selecting a topic
Both students and lecturers from IP Paris can propose research project topics. Once a student and a lecturer (advisor) agree on doing a research project together, the advisor sends an email to the program coordinators and the student affairs advisor with the name of the student, the project description, and the start and expected end dates of the project. Once the research project has been approved by the program coordinators, it can start.Doing the research project
The student should spend 10 days in a lab working with the advisor on the selected research topic. At the end of the research project the student should write a short document summarizing the contribution. The advisor grades the project as she or he wants, for example based on the short document, a defense, or the outcome of the project.
The grading scale is as follows: the range [0;10[ means a failing grade; the [10;12[ is reserved for underwhelming projects; the range [12;14[ is for OK projects that have not met all expectations; the range [14;16] is for good projects; the range ]16;18] is for really good projects that have exceeded expectations; the range ]18;20] is reserved for the best projects that one could do.
Internships
Time frame for M1
The master’s M1 program can optionally include an internship of at least 3 months. (The internship can be longer than 3 months, but it will count only 15 ECTS.) The internship should take place in the second half of the study year. The internship should terminate before the start of the next semester.Time frame for M2
The master’s M2 program includes an internship of at least 5 months and at most 6 months. The internship should take place in the second half of the study year.Finding an internship
An internship may take place in a company or a research lab, in France or abroad. Students who aim to pursue a PhD are encouraged to choose a research-lab internship, as it can naturally lead to a doctoral project.
You are responsible for securing your internship. The most effective ways include:
- Browse the offers posted on our dedicated page: dataai.telecom-paris.fr/internships
- Contact researchers working on topics that match your interests, especially within the IP Paris ecosystem. Many researchers already have funding for interns.
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Watch the main mailing lists where internship calls are regularly shared:
• MADICS
• GDR IA
• AFIA job board - Leverage general professional networks such as LinkedIn. Use keyword alerts and alumni contacts to surface relevant opportunities.
In addition, the member schools of IP Paris regularly organize internship forums, and program coordinators will circulate selected offers when they become 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 of research nature (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 sends the offer to the program coordinators and the internship coordinator by email to validate the topic (in particular about its research nature).
- 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 the pedagogical coordinators (and put the program coordinators in Cc).
- The student finds an academic evaluator from IP Paris, who is different from the advisor of the internship. To this purpose the student should contact Data AI lecturers with knowledge on the topic of their internship. If the student fails to find an academic evaluator s/he can contact the coordinators for help. If the lecturer agrees to be the academic evaluator, the student informs the internship coordinator 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 academic evaluator, 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 IP Paris. The “Organisme d’accueil” is the host institution. The “Stagiaire” is the student. The “enseignant référent” is the academic evaluator. The “tuteur” is the internship advisor. The program coordinators do not appear in this agreement.
Doing the internship
The student does the internship under the guidance of the internship advisor. The academic evaluator does not intervene, consult, collaborate, or co-organize. She or he mediates in case of disagreement between the student and the internship advisor. Before the end of the internship the student must contact the academic evaluator for the organization of the defense.Defending the internship
The internship finishes with a report and an oral defense. Both have to be in English.
The internship report has usually 30-60 pages for M2 internships and 20 pages for M1 internships. The student sends the report to the academic evaluator around 1 week before the defense. The report should clarify the following points: the general context, the problem studied, your contribution, the details of the contributions, the arguments supporting their validity or the interest of your contribution. These different topics could be (but don't have to be) the sections of your report. They might not apply to your specific case.
The defense consists of a talk by the student of 20 minutes, followed by questions by the jury. The defense is attended by the academic evaluator, ideally one other lecturer of the DataAI program, and other people if desired. The presence of the internship advisor is desirable. If the internship advisor cannot come, she or he shares feedback about the internship with the academic evaluator. In exceptional cases, the defense can take place via video-conference. The defense should take place not more than 3 weeks before the end of the internship, and not more than 1 month after the end of the internship. The defense has to happen during the current year of study. The student is in charge of organizing the defense. The defense takes place at the institute of the academic evaluator. The academic evaluator will help book a room.
The internship is graded by the academic evaluator in coordination with the internship advisor, by taking into account the quality of the work, the report, and the defense. Following the defense, the academic evaluator fills in the Internship Evaluation form on Synapses (for Télécom Paris lecturers) or sends her/his assessment to the internship coordinator by email.