Master Year 2 Data and Artificial Intelligence
| Year | Master Year 2 |
| 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 advanced knowledge in data management and artificial intelligence.
Asset n°2
Gain practical experience with a research Internship.
Asset n°3
Pursue a PhD or research career in the private or public sector.
The two-year Data 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 ethics in AI.
In the second year, students will build more advanced knowledge and complete a research internship.
Objectives
The Master’s program aims to enable 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 exploring the technical and scientific challenges of processing large data and knowledge bases
- 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 equipped to:
- Build a successful 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, e.g., in research and development
To validate the Master Year 2, 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 Master year 2 (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)
Students should not take two courses with overlapping topics, for details see the course descriptions.
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
Academic prerequisites
Completion of the first year of a Master in computer science or related field at Institut Polytechnique de Paris or equivalent in France (Engineering School) or abroad.
Language prerequisites
English: proof of English proficiency is required (TOEFL results, language course or 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
Estimated fees for 2022-2023
- 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" of the FAQs
Find out more about scholarships
Applications and admission dates
Coordinator
Program office
General enquiries
The two-year Data 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 ethics in AI.
In the second year, students will build more advanced knowledge and complete a research internship.
Objectives
The Master’s program aims to enable 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 exploring the technical and scientific challenges of processing large data and knowledge bases
- 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 equipped to:
- Build a successful 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, e.g., in research and development
To validate the Master Year 2, 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 Master year 2 (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)
Students should not take two courses with overlapping topics, for details see the course descriptions.
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
Academic prerequisites
Completion of the first year of a Master in computer science or related field at Institut Polytechnique de Paris or equivalent in France (Engineering School) or abroad.
Language prerequisites
English: proof of English proficiency is required (TOEFL results, language course or 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
Estimated fees for 2022-2023
- 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" of the FAQs
Find out more about scholarships