Data AI 2024-2025
You can take a look at the slides for Data AI introduction meeting or go to the moodle for the Data AI basic course. Data AI will start on September 2 with a kickoff meeting and some introductory courses. The dates for vacations are:- from October 27 to November 3
- from December 23 to January 5
- from February 24 to March 2
- from April 19 to April 27
Overview
The DataAI study track is a two-year master’s program at the Institut Polytechnique de Paris to prepare students for a PhD. It is concerned with Artificial Intelligence (AI) and large-scale data management. To apply, you can browse the official IP Paris webpage for the the M1 and for the the M2.
The program is taught in English. It teaches students the basics of Machine Learning, Logic, Big Data Systems, and Databases, before diving into applications in advanced machine learning, symbolic AI, swarm intelligence, natural language processing, visual computing, and robotics. Students can choose from a wide variety of courses, including on the mining of large datasets, big data processing systems, reinforcement learning, GPU programming, semantic networks, cognitive modeling, self-organizing multi-agent systems, autonomous navigation for robots, text mining, image understanding, as well as social issues in AI.
The program has a focus on research, and aims to familiarize students from the beginning with scientific work with scientific projects and internships. This way, students are optimally prepared for doing a PhD.
- Language of instruction: English
- ECTS: 120
- Orientation: PhD
- Duration: 1 year (M2) or 2 years (M1+M2)
- Start: September
- Course Location: Quartier Polytechnique, Palaiseau, France
Educational objectives
The master’s program will equip students with the fundamental knowledge, technical skills and concrete applied methodologies for making machines more intelligent. In particular, students will acquire experience in using and developing data-supported smart services and tools for data-driven decision making and will learn how to master technical and scientific challenges in processing large data and knowledge. The students will be taught to solve theoretical problems as well as applied ones, to present their work both in oral presentations and in written reports, to analyze the bibliography and identify open research directions, to work independently as well as in a team, to identify and seek appropriate resources for advancing their work, whether theoretical or applied, and to take initiatives.
Career prospects
The combination of big data and artificial intelligence in all of its forms is an active field of research. Students will be prepared for research in Robotics, Image processing, Machine Learning, Web technologies, the Social Web, Data Analytics, Big Data Management, Knowledge Base Management, Information Extraction, Information Retrieval, Databases, Data Warehousing, Knowledge Representation, and Distributed Data Management.
This master is a research master and students are strongly encouraged to a PhD after the master. The Institut Polytechnique de Paris and the associated research labs (Inria, CNRS, etc.) offer a great environment for a PhD, and our program is an optimal preparation for this path.
Admissions
Following our program requires a solid background in both math and CS with a good knowledge of at least one programming language. While a bachelor in math or computer science is highly recommended, we are open to diverse profiles as long as they can demonstrate their knowledge of math and computer science.
As courses are in English, the applicants need to justify their capacity to follow a course in English. We don't require a specific certificate or a specific grade but the candidate are encouraged to include a test (e.g. IELTS, TOEFL), be native speakers or have followed previous programs in English.
More information and the technical information to apply are available on the official IP Paris webpage for the the M1 and for the the M2.
If you have questions about the specific rules or organization of the Data AI master you can contact the master team at the address master-dataaiFrequently Asked Questions
- Do I need a recommendation? Yes, recommendation letters are important for us to know the candidates better. The only exception are for students coming from one of the IP Paris schools who can leave the recommendation empty (the system asks for a valid mail address but you can put not_needed@ip-paris.fr).
- Can I have a scholarship? Can you submit my name to the Eiffel scholarship? Students are encouraged to apply to the Data AI PhD track program but the Data AI master does not have scholarships to offer nor does it submits name to scholarships.
- I have a bachelor in economics/math/cs/biology can I still apply? You can always apply and that is the only way to get an answer on whether you will be accepted but, as stated above, "following our program requires a solid background in both math and CS with a good knowledge of at least one programming language". If the jury fears that you don't have the required background they will not accept you. Note that CS is not just coding.
- Can I still apply if I have two bachelors/a bachelor and a master/two masters/ a bachelor from 10 years ago? You can always apply if you have at least one bachelor (see question above).
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What should I put in my motivation letter (a.k.a. statement of
purpose)? A motivation letter should demonstrate your
motivation and therefore there is not one good answer as the
best motivation letter will be different for different
applicants. Here are some important information that the jury is
generally looking for: why did you apply to our master? what do
you plan on doing after the master (long and short terms),
e.g. are you planning on doing a PhD? are there specific topics
that interest you? do you have experience with research? if you
have a background not in CS, how do you plan on catching up?
The length of the motivation letter can vary on what the candidate has to say but generally one page is enough. - I have an average of ... at university ..., would I be selected? Only the jury can get a definitive answer so one would need to actually apply to get an answer. We receive a lot of applications, much more than the number of positions that we have to offer and therefore the master is highly selective. We cannot give a "grade threshold" on acceptance as different countries, different universities and even sometimes different curricula have different grading scales or different grading systems. Furthermore, acceptance relies on more than just an average and on more than just grades.
- Should I apply to the M1 or the M2? Only the jury can get a definitive answer, if you have doubts you can apply to both but the M2 is reserved for students that already have a master degree or 240 ECTS (if you don't know what that means you probably don't have 240 ECTS). Side note: if you already hold a master degree please state in your personal statement why do you need a second one (as opposed to apply to a PhD directly).
- When can I apply? Admission dates can be found on the IP Paris website. If the dates for next year are not already published it is because we don't already have them but the dates for the current year should give a good estimate.
- When do the course start? Courses generally start on the first week of September.
- I have difficulties with the admission website? Here is a short video showing you how to create an application.
- Can do follow the program in "alternance"? Can I do the program while I am working for a company? The program is meant to be a full time occupation. We don't offer "alternance" and we strongly advise against having a part time or a full time job on the side.
Institutional partners
- ENSTA Paris
- École Polytechnique
- Télécom Paris
- Télécom SudParis
Each course of the program takes place in exactly one of these schools, all schools offer at least one course of the program, and all schools are located at the same campus in the city of Palaiseau in France, just south of Paris. The final diploma will be delivered by Institut Polytechnique de Paris.