Course description

Master 1 students start the year with a 3 to 4 weeks sequence, called Boot camp, which focuses on full-time programming training.

Course objective

Main objectives of the boot camp are:

• for students, to meet each other and build a group dynamic;
• that they understand the value of the Digital Sciences curriculum;
• the students are coming from various backgrounds and have different skill levels, so it is crucial to homogenize their computer skills and to give some basic theoretical bases for artificial intelligence.

Course description

This course will cover the main concepts of artificial intelligence. For each part of the course, after an individual learning and study on a specific use case, the students will have to present their solution to the problem they had to solve.

• Introduction, what is Data
• Supervised Classification
• kNN, Linear models, Decision trees, Naïve Bayes
• Model selection
• Regression
• Unsupervised learning
• Neural Networks and Deep Learning
• SVM, Ensembles (forest)
• Social Networks
• Text mining, Natural Language Processing
• Recommendation systems

Course objective

Students will be able to choose the best-fitted tools and algorithms in order to solve a particular problem.

Key concepts

Artificial Intelligence

Course description

This course will provide an advanced introduction to the field of data science, with a focus on network and spatial data. Topics will cover data project management, data collection, cleaning, analysis and visualization, network analysis and representation, and work with spatial data. The course will have a strong focus on hands-on sessions and personal projects.

Network part:

Why focus on network data? Over the past century, network studies have had significant impact in disciplines as varied as mathematics, sociology, physics, biology, computer science or quantitative geography, giving birth to Network Science as a field of itself. With the recent rise of social networks in the last decade, their use has now become widespread in the digital world. Here we will provide an introduction to the field of Network Science, from the theoretical foundations (generating, analyzing, perturbing networks) to the practical hands-on part (analysis and visualization of real-world networks).

Network topics covered in 2021-2022 (changes may be present in 22-23):

a. How to construct networks from real data?

b. How to analyze networks? (centrality measures, community detection, statistical analyses etc.)

c. How to visualise networks?

d. Dynamics and spreading phenomena on networks (epidemics / information spreading, diffusion)

e. How do networks wirings change in time? (network robustness, temporal networks)

f. How to represent more complex network data? Multilayer, multiplex networks.

Students will select, analyse and present a network of their choice as part of a personal project for the course. They will also choose an advanced topic in network science & big data for which they will make a presentation in a reverse classroom setting. They will in particular contribute to a wikipedia page about that topic.

Course objective

At the end of the course the students will have gained intuition to analyze real-world data. They will be able to use Python and R for statistical analyses and working with data. They will know practical tools and packages to work with spatial data, as well as network visualization tools. Finally, they will have obtained good practices for code and data management.

Skills

Knowledge skills

• Network theory
• Mathematical models
• Dynamic processes
• Algorithms
• Data analysis
• Data visualisation

Operational skills

• Network mining
• Network analysis
• Network visualisation
• Data curation

Cooperative skills

• Collaboration for personal and group projects
• Raise issues on github projects from other students
• Peer-evaluation
• Joint scientific presentations
• Peer-to-peer learning

Course description

Introductory course to robotics. Robotics is about building machines that can act in the real world and often substitute human action. Together, students and teachers will decipher how robots work and why engineers built them the way they are. The course will be based on hands-on manipulation of robots to learn basic concepts related to sensing, acting, and planning

Course objective

By manipulating robots, students will discover what robots are made of and understand how each component works. In small teams, students will program robots to complete simple tasks such as line following, obstacle avoidance, and automated drawing; which will help them understand some of the basic concepts of robotics: feedback loops, odometry, trajectory planning, direct and inverse model. The final project will be a robotic sumo competition.

Key concepts

Sensors; Actuators; Planning; Feedback loop; Odometry; Inverse model; Soft robotics

Skills

Knowledge skills

• Closed loop control
• Trajectory planning
• Odometry
• Inverse model
• Decision making
• Software interruptions

Operational skills

• C++ and Python programming
• Design of algorithms
• Practical understanding of the types of sensors and actuators available and their operating principle.

Reflective skills

• Understand the links between scientific concepts and disciplines
• Hypothesis-Prediction-Experiment-Test-Refinement Loop

Cooperative skills

• Team management
• Group problem-solving
• Collaborate together towards a common goal

Key concepts

Virtual experiences; Ubiquity; Immersion; Embodiement; New Media; Interactivity; Avatar and Bots; Design, products and tools; Ethical analytics; Modelisation

Skills

Knowledge skills

• Knowledge of the production steps of a digital work
• Understanding of the complementarity relationships between input and output sciences (sciences and social sciences)
• What do cultural and ethical approaches bring?
• Proficiency in specialized interactive media vocabulary
• Understanding of stakeholder tensions and communities

Operational skills

• Identify and organise accurate informations about a topic with relevant scientific publications
• Imagine a modification or a totally new versions of an immersive product
• Create a little virtual world with inhabitants

Reflective skills

• Analysis of a media system from the point of view of usage
• Identification of the ethical principles and philosophical conception underlying an immersive or playful experience
• Take a step back from the accompanying speeches and market expectations
• Imagine the untapped or under-tapped potential of a technology
• Understand the interactions between different media regimes

Course description

The class is in project mode. It starts with two project emergence sessions. 2 project review sessions punctuate the progress. A presentation of the documentation and an exhibition of the completed prototype conclude the work. The meetings with mentors are organized by each project team. The course leads students to carry out a personal, individual or group project. The rules are that the project:

• is a stage of the personal learning path, journey and helps to achieve a goal related to the personal and professional development project
• is a deepening of one of the Teaching Units, and to present explicit research aspects
• has a "tech for good" goal: contribution to the commons of knowledge, ethics by design, sustainable development, learning planet, in relation to the ideals and values of the CRI
• involving a relationship with a mentor and different resource persons, freely sought
• is interdisciplinary, and, optimally, has links with LeSc and LiSc
• leading to a functional and documented prototype

Course objective

• train in project management and design (roles, cooperation, roadmap, need and expectation of users, business model and value produced).
• give a space of freedom to deepen a field and specialize himself or herself
• develop an interdisciplinary cooperation
• develop personal relationships and address book
• fully experiment of the learning by doing concept
• enrich his-her skills portfolio
• assume all aspects of the documentation, to make the project shareable, transmittable, continuous and replicable

Key concepts

Documentation; Prototype; Workgroup; Skill portfolio; Path of learning; Value; Project; Design; Cooperation

Skills

Knowledge skills

• learn about an inclusive and sustainable innovation
• learn how to define needs and expectation, and document it
• search the relevant information (field research, interviews, technical tests, feedbacks…)

Operational skills

• lead a project, from idea to prototype
• organize a shareable documentation
• achieve a technical and social project, producing value for a living system

Reflective skills

• establish a relation between an open infrastructure, application or machine, and the achievement of social goals
• take into account the ecological footprint of digital projects
• be able to explain and justify technical and scientific choices, in front of various interlocutors

Cooperative skills

• share the work
• recognize the value of contributions, knowledge and know-how (their own and reciprocally that of others)

Course description

In recent years open science has lowered barriers to the flow of information amongst scientists, from scientists to the public, and most radically, has encouraged public input into scientific process, especially in the natural and health sciences. “Open science, open innovation, open to the world” are “the three O’s” proposed by the European Commissioner for Science Research & Innovation to think the next political agendas because science and innovation are playing an increasingly important role in shaping the future. What is today different from what it has ever been regarding science production and the demand of science with and for society is growing worldwide. What is inherently closed in the “scientific production factory” that has to be open and how to open it?

In the scientific organizations, much of the focus with open science is usually how to develop alternative open publication systems, or else how repositories can be made available to host all kinds of open data and content. On the one side, there is still strong dependency on tools and platforms run by commercial, corporate entities. On the other side, the characteristics of commons-based peer production systems persist with its unbalanced power relations.

Open science encompass also citizen science, understood as the involvement of community contributors in the research process. Non-researchers participate in some aspects, mainly data collection and processing. But as it evolves, citizen science projects have become more and more diverse and so is the contribution of the crowd, even parts of the research process are still not accessible.

The students will be invited to investigate the various ways by which open science and citizen science, serve research, learning and innovation for good, the revolution that it brings in the scientific organization and how to maintain open the research process.

Course objective

Students will be invited to experiment what is open science, through practice, familiarization with research projects experiencing different levels of openness to the crowd and group reflection. In doing so, they will be prepared to open their view on science and research, position themselves regarding the evolution of citizen science and innovation in the science.

At the end of the course, students will:

• be more familiar with the open publication system
• master and understand more deeply the steps of the research process
• reflect on what is science and dispose of tools to open scientific processes
• be prepared to extreme citizen science

Key concepts

Open science; Citizen science; Scientific process; Experimental process ; Scientific quality

Skills

Knowledge skills

• Familiarization with Open Access publishing and Open Access repositories
• Principles of Open Data management (analysis, use, and reuse of data), metadata, and data dissemination (sharing and granting access to data)
• Dissection of a research process and familiarization with professional research conduct which include research management skills, research integrity and ethics skills, and IPR and legal skills

Operational skills

• How to share code and data
• Collective intelligence

Reflective skills

• The impacts of opening science and the principle of equity, freedom and inclusivity
• The social role of science
• The ethics of science
• The knowledge economy

Course description

These courses will allow AIRE master students to learn around issues related to the use of computer tools in various fields. Students may also select one or more Collège de France’s inaugural courses in Computer Sciences, and underly explicit issues to present them to the rest of their classmates.

Course objective

The objective is to lead students to question the use of computer tools and their technological and societal repercussions.

Skills

Knowledge skills

• understand the role of computer tools in research
• identify the importance of computing in interdisciplinarity
• identify areas of knowledge and action concerned by IT

Operational skills

• Be able to analyse the content of a conference: guiding principle, key concepts, models, graphs, fields of application

Reflective skills

• Critical thinking

Cooperative skills

• conduct work group
• organize the division of roles and the complementarity of tasks
• time management (reading, discussion, setting up an analysis strategy, shared notes, collaborative writing, cross-referencing, P2P evaluation )

Course description

The course starts with an inventory of issues, coming from the students: what are the crucial aspects of digital transformation that require the vigilance of IT professionals and scientists ? What are the visions of digital and ecological transformation? What do we believe, hope, say, make, about the future of society? What do we learn from the IT practices, the observation of IT professional circles, the policy making and public debates?

Once the topics have been chosen, either by group or individually, students conduct their own research. They analyze books, articles, and interviews, and organize the results using the controversy method. Meetings with researchers and experts facilitate the work. The results are published and discussed with guests who best represent the topics covered.

Course objective

• Establish a space to think about "Science and society" issues
• Provide analytical tools from the humanities and social sciences
• Link this research to the construction of professional and citizen responsibility (Social- Scientific- Entrepreneur)
• Introduce "Ethics by design" into technical practices

Key concepts

Digital transformation; Climate change; Artificial Intelligence and society; AI for Humanity; Ethics by design; Future of work; regulation, fairness and transparency; Controversies studies

Skills

Knowledge skills

• In the field of digital transformation, to identify a controversy topic
• Collecting appropriated data (Reading, interviews, content analysis) to explore and explain
• Write a first literature review

Operational skills

• Know how to represent a complex problem with a mind map or a sketch note
• Design prospective scenarios (data and information-based)
• Carry out a demonstration or case study, to assess the impact of technology on societies (e. g. prove an algorithmic bias, measure an ecological impact, try to objectively assess the "ethical value" of a technology (TOU of a software or platform, bioinformatics, VR and health, Deep face and DeepFakes ...)

Reflective skills

• Question techniques and sciences with regard to the challenges of sustainable development
• Deliberate on the good matching of personal engagements and human values to technical practice.

Cooperative skills

• Have a behaviour open to "collective intelligence", explore the same topics with different interpretations, compare and cumule interpretations
• Modify his/her points of view, integrating new perspectives
• Build plural views, impossible to design without collective intelligence
• Learn to think from the other's place

Course description

The Advanced Computing module is organised as a set of different courses and workshops supervised by specialists so that students can have a theoretical and hands-on overview of important digital topics.

The topics covered can be fundamental subjects to know for computer scientists (modelisation, databases, security, network...) or much more advanced topics (quantum computing, blockchain, IoT...)

Students who wish to explore one or more specific themes will have later the opportunity to do so with a mentor during projects or internships.

Course objective

Students must acquire and develop the intellectual agility so they can discover and implement new concepts, either simple or advanced, in digital sciences.

Key concepts

IT concepts and practise: fundamental and advanced

Course description

This course allows students to review the knowledge required for project management:

• requirement analysis
• design thinking methodology
• specifications (analysis, writing, evaluation…)
• technical and business writing
• acceptance phase
• risk management
• agile methodologies
• quality management
• maintenance
• testing tools and methodologies
• cost, human resources, tasks, time management
• security, ethics by design
• managing a projects portfolio
• methodological and IT tools
• etc.

Research, product or service type projects will be discussed.

Course objective

Students will be familiar of the main lines of project management so they can apply what they have learned to their own projects or those in which they will participate.

This course will therefore include a theoretical but above all practical part, in the form of use cases, serious games or follow-up sessions on students' real projects.

The management of multiple projects will also be discussed.

Key concepts

Project management

Course description

Masterclasses will be organized with specialists in several disciplines.

Course objective

In addition to discovering or deepening the subjects discussed by the invited speakers, students will develop their skills related to the organisation of an event.

Key concepts

Event organisation; misc. subjects

• M1 students will perform a long internship, 5 to 6 months, in France or abroad. This is their chance to apply what they learnt during their first semester intensive courses, to explore their passions, to gain precious experience and to learn how a digital project is carried out in a real environment(R&D department, Research lab, NGO).