Master

Data and Artificial Intelligence


Overview


The DataAI study track is a two-year master’s program at the Institut Polytechnique de Paris. It is concerned with Artificial Intelligence (AI) and large-scale data management. The program is taught in English. It teaches the 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: there is a scientific project in the first year, and an internship in the second year. This way, students are optimally prepares for scientific work, a PhD, or research and development positions.

 

Language of instruction: English
ECTS: 120
Orientation: Research and Industry
Duration:  1 year (M2) or 2 years (M1+M2)
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.


Program structure


All courses are 24h, count 2.5 ECTS, and are validated by labs, presentations and/or exams.

For the 2-year program (M1+M2), the student has to pass all mandatory courses (15 ECTS), 55 ECTS in the optional courses, 15 ECTS in courses of other programs, a research project of 5 ECTS, and a research internship of 30 ECTS.

For the 1-year program (M2), the student is exempted from the mandatory courses that she/he has already taken. The student has to do a research project of 5 ECTS, a research internship of 30 ECTS, 5 ECTS in courses of other programs, and optional courses to arrive at 60 ECTS in total.

There is no predefined order in which the courses have to be taken, except that the mandatory courses have to be taken before the optional ones.

  • Machine Learning (3 equivalent courses for choice)
  • Logic (2 equivalent courses for choice)
  • Big Data Systems (5 equivalent courses for choice)
  • Databases (2 equivalent courses for choice)
  • Softskills (2 courses: Softskills seminar and Social issues in AI)

Advanced Machine Learning
Advanced Machine Learning and Autonomous Agents
Large scale Machine Learning
Reinforcement Learning
Probabilistic Models and Machine Learning
Programming with GPU for Deep Learning

Symbolic AI
Efficient resolution of logical models
Semantic Networks
Algorithmic information and artificial intelligence
Constraint programming
Cognitive approach to NLP

Collective and social intelligence
Multimodal Dialogue
Emergence in complex systems
Self-Organising Multi-Agent Systems

Data Mining
Data Stream Mining
Factorization-Based Data Analysis
Mining of Large Datasets
Graph Mining
Graph mining and Clustering

Robotics
Navigation for autonomous systems
Learning for robotics
Architectures for robotics

Text and language
Knowledge Base Construction
Text Mining and NLP
Machine Learning for Text Mining

Visual computing
Image understanding
Image mining and content-based retrieval
Data Visualization
Basics of image processing and analysis
Topological Data Analysis


Laboratories involved



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.

Students who wish to pursue a PhD afterwards are more than encouraged to do that. 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. The program will also allow students to apply to positions in the industry, mostly in research and development labs.


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.


Chairs and partnerships


  • Chaire Data Science and Artificial Intelligence for Digitalized Industry and Services (Télécom Paris et al.)
  • Chaire Data Engineering et Intelligence Artificielle pour la Banque et l’Assurance (Télécom Paris et al.)
  • Chaire Pédagogie des Sciences de la Donnée (Télécom Paris et al.)
  • Chaire Data Science pour le e-commerce (Télécom Paris et al.)
  • Chaire Methods and Algorithms for Artificial Intelligence (Télécom Paris et al.)

Admissions


Academic Prerequisites

For the 2-year program (M1+M2): A Bachelor’s degree in Computer Science or equivalent, with good background knowledge in mathematics.

For the 1-year program (M2): A diploma of at least 4 years of study in Computer Science or a neighboring field. Engineering students of the schools of IP Paris can apply for this program for their 3rd year of studies (4th at Ecole Polytechnique).

Alternatively, you can apply for the PhD Track Computer Science, and choose DataAI as your primary focus of studies.

Language prerequisites

The entire program is taught in English, and proof of English proficiency is a prerequisite for the program. Any proof of English proficiency will do (TOEFL, language course, language test,…). Native speakers of English and students who studied in English do not need a certificate of English proficiency.

Documents

For the list of documents, please see the Application guidelines for a master’s program at IP Paris.

All document have to be in English or in French. We insist that the cover letter be in English.

Application timeline

Deadlines for the Master application sessions are as follows:
– First session: February 28, 2020
– Second session: April 30, 2020
– Third Session (optional): June 30, 2020 (only if there are availabilities remaining after the 2 first sessions)
Applications not finalized for a session will automatically be carried over to the next session.

You shall receive an answer 2 months after the application deadline of the session.
You can check your application status by logging in your candidate space.

The courses start in September 2020.


Tuition Fees per year


International Master: EU students : 4250 euros / Non-EU students: 6250 euros


Contact


Louis Jachiet & Goran Frehse
email