Institut Polytechnique de Paris
Ecole Polytechnique ENSTA ENSAE Télécom Paris Télécom SudParis

Master Year 1 Data and Artificial Intelligence

Master Year 1 Data and Artificial Intelligence
Year

Master Year 1

Program

Data and Artificial Intelligence

ECTS Credits

60

Language

English

Orientation

Research and Industry

Location

Palaiseau Campus

Course duration

12 months, full time

Course start

September

Degree awarded

Master’s degree obtained on completion of a second year of Master

WHY ENROLL IN THIS PROGRAM?

Asset n° 1 

Acquire strong basic knowledge in data and artificial intelligence

Asset n°2

Conduct a research project

Asset n°3

Learn from renowned faculty members

The two-year Data and Artificial Intelligence Master’s program covers artificial intelligence (AI) and large-scale data management. Students will acquire 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 range of courses including mining 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 and image understanding, as well as social issues in AI.

This first-year program provides students with core knowledge and requires them to conduct a research project.

Objectives

The Master’s program enables students to:

  • Acquire the fundamental knowledge, technical skills and concrete applied methodologies to make machines more intelligent
  • Gain experience in using and developing data-supported smart services and tools for data-driven decision-making, while also exploring technical and scientific challenges in processing large data and knowledge
  • Solve theoretical and applied problems, present their work in oral presentations and written reports, analyze a bibliography and identify open research directions, work independently and in a team, identify and seek appropriate resources for advancing their work

Students who successfully complete the 2-year program will be able to:

  • Embark on a research career in robotics, image processing, machine learning, web technologies, social web, data analytics, big data management, knowledge base management, information extraction, information retrieval, databases, data warehousing, knowledge representation, and distributed data management
  • Pursue a PhD at Institut Polytechnique de Paris
  • Build a career in industry, especially in research and development departments

To validate the M2 year, a student must accomplish the following:

  • fulfill all the Data AI mandatory course 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

Students must validate at least one course for each of the following groups, before the end of the M2 year (courses completed in M1 count as validated):

  • 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)
    •  

All Data AI courses are 24h, count 2.5 ECTS, and are validated by labs, presentations and/or exams. A course is validated when the student obtains a grade of 10/20 or higher.

List of available courses:

  • Big data infrastructures
  • Probabilistic Models and Machine Learning
  • Learning for robotics
  • AI Ethics
  • Semantic Networks
  • Databases
  • Logic & Knowledge representation
  • Self-Organising Multi-Agent Systems
  • Machine Learning in High Dimension
  • Logics and Symbolic AI
  • Database management systems
  • Graph Mining
  • Machine Learning: Shallow & Deep Learning
  • Constraint programming
  • Machine & Deep  Learning Introduction
  • Efficient resolution of logical models
  • Graph mining and Clustering
  • Text Mining and NLP
  • Machine Learning for Text Mining
  • Basics of image processing and analysis
  • Data Stream Mining
  • Data Visualization
  • Reinforcement Learning
  • Navigation for autonomous systems
  • Multimodal Dialogue
  • Systems for Big Data
  • Kernel Machines
  • Architectures for Big Data
  • Image understanding
  • Knowledge Base Construction
  • Programming with GPU for Deep Learning
  • Big Data Processing
  • Emergence in Complex Systems
  • Cognitive approach to NLP
  • Algorithmic information and artificial intelligence
  • Softskills seminar (M2 only)
  • Mining of Large Datasets
  • Image mining and content-based retrieval
  • Advanced Machine Learning and Autonomous Agents
  • Topological Data Analysis
  • Machine Learning

 

Admission requirements

Academic prerequisites

  • Completion of a Bachelor of Science at Institut Polytechnique de Paris or equivalent in France or abroad
  • Have good background knowledge in mathematics

Language prerequisites

English: proof of English proficiency is required (TOEFL results, language course, language test) except for native speakers and students who previously studied in English.

How to apply

Applications can be submitted exclusively online. You will need to provide the following documents:

  • Transcript
  • Two academic references (added online directly by your referees)
  • CV/resume
  • Statement of purpose

You will receive an answer in your candidate space within 2 months of the closing date for the application session.

Fees and scholarships

  • EU/EEA/Switzerland students: 4243€
  • Non-EU/EEA/Switzerland students: 6243€
  • Engineer students enrolled in one of the five member schools of Institut Polytechnique de Paris (Ecole polytechnique, ENSTA Paris, ENSAE Paris, Télécom Paris and Télécom SudParis): 159€
  • Special cases: please refer to the "Cost of studies" section of the FAQs

Find out more about scholarships

Applications and admission dates

Coordinator

Goran Frehse

Louis Jachiet

Program office

Danielle Deloy

General enquiries

master-admission@ip-paris.fr

Description

The two-year Data and Artificial Intelligence Master’s program covers artificial intelligence (AI) and large-scale data management. Students will acquire 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 range of courses including mining 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 and image understanding, as well as social issues in AI.

This first-year program provides students with core knowledge and requires them to conduct a research project.

Objectives

The Master’s program enables students to:

  • Acquire the fundamental knowledge, technical skills and concrete applied methodologies to make machines more intelligent
  • Gain experience in using and developing data-supported smart services and tools for data-driven decision-making, while also exploring technical and scientific challenges in processing large data and knowledge
  • Solve theoretical and applied problems, present their work in oral presentations and written reports, analyze a bibliography and identify open research directions, work independently and in a team, identify and seek appropriate resources for advancing their work

Students who successfully complete the 2-year program will be able to:

  • Embark on a research career in robotics, image processing, machine learning, web technologies, social web, data analytics, big data management, knowledge base management, information extraction, information retrieval, databases, data warehousing, knowledge representation, and distributed data management
  • Pursue a PhD at Institut Polytechnique de Paris
  • Build a career in industry, especially in research and development departments

To validate the M2 year, a student must accomplish the following:

  • fulfill all the Data AI mandatory course 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

Students must validate at least one course for each of the following groups, before the end of the M2 year (courses completed in M1 count as validated):

  • 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)
    •  

All Data AI courses are 24h, count 2.5 ECTS, and are validated by labs, presentations and/or exams. A course is validated when the student obtains a grade of 10/20 or higher.

List of available courses:

  • Big data infrastructures
  • Probabilistic Models and Machine Learning
  • Learning for robotics
  • AI Ethics
  • Semantic Networks
  • Databases
  • Logic & Knowledge representation
  • Self-Organising Multi-Agent Systems
  • Machine Learning in High Dimension
  • Logics and Symbolic AI
  • Database management systems
  • Graph Mining
  • Machine Learning: Shallow & Deep Learning
  • Constraint programming
  • Machine & Deep  Learning Introduction
  • Efficient resolution of logical models
  • Graph mining and Clustering
  • Text Mining and NLP
  • Machine Learning for Text Mining
  • Basics of image processing and analysis
  • Data Stream Mining
  • Data Visualization
  • Reinforcement Learning
  • Navigation for autonomous systems
  • Multimodal Dialogue
  • Systems for Big Data
  • Kernel Machines
  • Architectures for Big Data
  • Image understanding
  • Knowledge Base Construction
  • Programming with GPU for Deep Learning
  • Big Data Processing
  • Emergence in Complex Systems
  • Cognitive approach to NLP
  • Algorithmic information and artificial intelligence
  • Softskills seminar (M2 only)
  • Mining of Large Datasets
  • Image mining and content-based retrieval
  • Advanced Machine Learning and Autonomous Agents
  • Topological Data Analysis
  • Machine Learning

 

Admission requirements

Academic prerequisites

  • Completion of a Bachelor of Science at Institut Polytechnique de Paris or equivalent in France or abroad
  • Have good background knowledge in mathematics

Language prerequisites

English: proof of English proficiency is required (TOEFL results, language course, language test) except for native speakers and students who previously studied in English.

How to apply

Applications can be submitted exclusively online. You will need to provide the following documents:

  • Transcript
  • Two academic references (added online directly by your referees)
  • CV/resume
  • Statement of purpose

You will receive an answer in your candidate space within 2 months of the closing date for the application session.

Fees and scholarships

  • EU/EEA/Switzerland students: 4243€
  • Non-EU/EEA/Switzerland students: 6243€
  • Engineer students enrolled in one of the five member schools of Institut Polytechnique de Paris (Ecole polytechnique, ENSTA Paris, ENSAE Paris, Télécom Paris and Télécom SudParis): 159€
  • Special cases: please refer to the "Cost of studies" section of the FAQs

Find out more about scholarships

Applications and admission dates

Coordinator

Goran Frehse

Louis Jachiet

Program office

Danielle Deloy

General enquiries

master-admission@ip-paris.fr