Course description :

In recent years AI applications have increasingly been dealing with understanding humans, their behavior, their decision-making process and even the human body, its comprehension and modelling for the creation of new interactive systems. Providing machines with intelligence for better collaboration, living and learning, have not only economic but also social consequences.
AI is thus currently at the center of discussions and debates that go beyond the digital domain because of the changes they may entail. This module aims to introduce students to Human Centered AI, clarify its definition and particularities in comparison to a more general AI. Students will have the opportunity to discover and discuss various applications of AI in robotics and interactive systems that transform our way of living, creating, working etc. in a variety of industries and fields (manufacturing, healthcare, art/creativity, sports, education, intelligent vehicles etc.)

Objectives :

• Introduction to the basic theoretical notions of AI
• Presentation of a wide range of applications
• Identification of the strengths and weaknesses of these applications through success and failure stories

ECTS credits : 1

Course description :

“Ethics and Privacy by Design” is a dynamic course on Artificial Intelligence (AI) combining the practical applications of constantly developing AI Systems in our everyday lives, personal, social, professional, with the ethical concerns inevitably arising in practice while using such Systems.
AI Systems and their results are primarily based on the access to, selection and input of relevant and appropriate data, accumulated in various ways, with or without the consent of their proprietors, ethically or unethically. Intellectual Property Rights questions regularly arise in the process of the development of AI Systems as well as liability issues.
While policy and lawmakers are currently struggling hard in order to keep pace with booming technologies, observance of fundamental ethics rules from the initial stage of the development of an AI System and App, from its “Day One”, as “built-in” features, whether in private or in public sector, is becoming more demanding than ever before in recent times. In such an explosively expanding environment, self-regulation and self-monitoring by the developers and users of AI Systems themselves appears to be the only effective way to safeguard that such new technologies will only be used for the benefit of all citizens equally and our society as a whole to make our lives better.
“Ethics and Privacy by Design” critically approaches all issues of AI Systems, raising ethical dilemmas in designing of AI software and examining practical results, benefits and risks. Finally, each participant’s personal ethics will decide the reasoned answer to such issues and dilemmas.

Course description :

The course focuses on all the notions of the python programming language for data treating and signal processing, as well as of the Robot Operating System (ROS) and the Gazebo simulator suite. It will allow the students to get introduced not only to the logic, but also the tools that will be used in the rest of the AIMove courses for the preparation of the student projects.

Objectives :

• Introduction to python programming for signal processing
• Familiarization with ROS and the Gazebo suite.

ECTS credits : 1

Course description :

This module deals with project management. The fundamental concepts are: typology of projects and structuring, graphic representation tools (Gantt chart, networks, critical road), anteriority between the tasks, critical path and lead-time margins, allocation of resources, levelling and smoothing of the load, costs, uncertain environment, project monitoring, organizational change and existing software. These notions are then illustrated through real-life cases. Particular attention will be paid to the notion of variability of tasks, which is inherent in human-centered engineering.

Objectives :

• Mastery of the fundamental concepts of project management
• Understanding of the roles of the different actors in a project: director, steering committee, project manager, business engineer, architect, partners, customers, etc.
• Following the 4 main stages of a project: 1) Structuring, 2) Planning, 3) Follow-up and 4) Feedback
• Understanding the limitations of existing software in assigning resources to differ-ent tasks
• Planning the initial costs of a project (CBTP), then tracking the project using the CBTE and CRTE curves

ECTS credits : 1

Course description :

This module aims to put students in the situation of a ‘movement engineer’ who aims to record, edit, and return movements for a given project. Beyond the pure description of biomechanics of the human body and the diverse available tools, the main goal will be to make students aware of what the nature of the ‘signal’ movement is and what needs to be done to respect him in their work. They will discover the aspects related to the facial, the eyes, the fingers, accessories, but also the matter of re-targeting, post-animation or style.

The main approach will be based on the concrete experience of a project conceived and directed by and for themselves, including a real recording session on a very high-quality platform. (1 day) On the one hand, they will be able to carry out all the necessary steps (preparation, distribution of tasks, possible tools, post-treatments, traps, attention to detail…), but also to experiment live the extreme subtlety and finesse of actual ‘movement’.

Objectives :

• Become aware of the extreme finesse of the “movement” material
• Experiment on a high-end professional tool
• Make a personal project
• Discover fundamental notions such as re-targeting, uncanny valley, VOR etc.

ECTS credits : 2

Course description :

Due to increasing digitalization in many fields, considerable amounts of data and images are accumulating in various domains (internet, marketing, logistics, biology, etc.). This has increased the need for automated and intelligent mining and exploitation of various kinds of data. In the meantime, numerous new algorithms (neural networks, SVM, boosting, Bayesian networks, etc.) have appeared in recent decades, and allow more powerful modelling and analysis than classical statistical linear methods.
The aim of this course is to provide a survey of these new so-called “machine-learning” algorithms, as well as their common theoretical and methodological framework, and their various types of applications.

Objectives :

• Statistical learning theory;
• Typology of applications: classification, regression, prediction, clustering and categorization, …
  shallow Neural Networks (multi-layer perceptrons, MLP);
• Deep-Learning with Convolutional Neural Networks; kernel methods and Support Vector Machines (SVM)
• Decision trees and Random Forests; boosting;
• Unsupervised learning for categorization (k-means, Kohonen’s topological maps);
• Decision trees and Random Forests
• Bootsting
• Unsupervised learning for categorization (k-means, kohonen’s topogical maps..)
• Evolutionary algorithms and other meta-heuristics.

ECTS credits : 3

Course description :

Deep learning meets a wide range of applications and is a key driver towards the development of autonomous and intelligent systems. Adaptability (exploration and adjustment to new conditions), dynamic learning (pattern identification, learning and representation of new structures), communication (interaction with humans through natural language, smart intercommunication among digital devices), perception of environment (object recognition, navigation, interaction), and control (agile and natural performance of tasks) have made significant progress as a result of the deep learning framework’s contribution.

The Deep Learning course is an introduction to the fundamental concepts and techniques of deep learning, with a focus on practical applications in Robotics. The main topics that are covered include: (i) Artificial Intelligence and Neural networks; (ii) Deep learning architectures: Convolutional neural networks (CNNs), Recurrent neural networks (RNNs), Autoencoders, Deep belief networks, Siamese networks, and others; (iii) Generative models: Generative Adversarial Networks (GANs), Auto-regressive models, Transformers; (iv) Computer vision; (v) Reinforcement Learning; (vi) Transfer learning; (vii) Optimization techniques. The course will involve hands-on coding exercises and mini projects, where students will implement and apply the concepts learned to real-world datasets.

Objectives :

This cours aims to provide with all the necessary knowledge for :

• To understand the fundamental concepts of deep learning, gain knowledge of state-of-the-art algorithms and be informed about the current research in deep learning.
• To learn how to design, train, and test deep neural networks for a range of applications including image recognition & segmentation, natural language processing, and mapping & localization.
• To acquire basic knowledge of deep learning frameworks such as Keras, Tensorflow and PyTorch.
• -To experiment and learn how to apply deep learning to real-world problems.

ECTS credits : 3

Course description :

Action recognition is the scientific and technological field in which learning machines are utilized to understand human motions and interact accordingly. Since « gestures are everywhere », there is a great range of applications, in Cultural and Creative Industries, Smart Manufacturing, Autonomous Vehicles, etc. This course presents a generic methodology for action recognition that has been validated through both industry-oriented projects. Topics include: (i) motion capturing, (ii) movement analysis and human pose estimation, (iii) feature extraction, (iv) modeling and representation using machine and/or deep learning, (v) continuous and early recognition.

Objectives :

This course aims to provide with all the necessary knowledge for: – identifying the appropriate sensor according to the movement scenario and application – estimating the human pose and extracting a skeleton from image sequences – extracting the appropriate features from the signal – representing the human motion using probabilistic methods that model the spatiotemporal dynamics that occur on the human body when performing a situated gesture – recognizing single-user an multiple-users action recognition through probabilistic methods and deep learning. »

ECTS credits : 1

Course description :

Understanding an image is a trivial task for human, but require complex analysis of colors, texture and geometry for a computer. This class will introduce computer vision and the key algorithms to extract semantic information, objects or structure. The course will cover introduction to pixel-level representations to segment textures, shadows, skins or to detect simple objects; model fitting techniques to extract geometrical information in the scene. Using (deep) learning techniques, we’ll build higher level representation used for classifying images, detecting humans, etc. Finally, we’ll introduce time and motion processing to track objects through time, and extract geometrical structure from motion.

Objectives :

The students shall acquire the following knowledge:

• Introduction to computer vision
• Texture segmentation (colors, light, texture)
• Clustering and model fitting (geometry)
• Estimation of image semantic (pixel-wise labeling)
• Object recognition (classification, etc)
• Time and Structure from Motion (tracking, reconstruction)

Class exercises will include:

Automatic scene segmentation (grass, concrete, etc.), object detection in images, tracking objects in videos, classifying images, etc.

ECTS credits : 3

Course description :

Earth Observation, remote and social sensing technologies produce massive data that require innovative data-intensive processes and computing facilities for the efficient extraction of information. The heterogeneity, the frequency, and, most importantly, the magnitude of those collections of data call for robust and large-scale data analysis methods, capable of supporting evidence-based science and sound decision-making.The aim of this course is to explore the topics of big data, data-driven and data analysis with focus on machine and deep learning techniques.

Objectives :

• Automated data analysis: large-scale machine learning, statistical learning, deep learning, transfer learning, data mining, feature engineering, optimization.
• Processing and computational infrastructure: real-time, incremental, CPU/GPU-based, cloud and distributed processing.
• Spatio-temporal analysis: filtering, morphological analysis, segmentation, signal processing, adaptive, local and global modelling.
• Applications: computer vision, text mining, time-series analysis.

ECTS credits : 3

Course description :

The course focuses on human motion analysis in interactive environments with special emphasis on serious games. This course will survey state-of-the-art techniques, in the industry and academia, related to the capturing, modeling, and analysis of human motion. It will involve an in-depth study of pattern recognition techniques and state-of-the-art human motion recognition algorithms focusing mainly on various Deep Learning approaches. Moreover, it will present techniques related to emotion recognition, through facial expression and body motion analysis, and engagement recognition in serious games. Finally, the course will introduce students to Artificial Intelligence (AI) algorithms for personalization and adaptation in serious games.

Objectives :

On completion of the course, the student should be able to:

• Understand basic pattern recognition approaches
• Apply various human motion recognition algorithms
• Utilize various algorithms for 3D human/hand pose estimation
• Apply human emotion recognition algorithms
• Discuss theories and models of user engagement
• Design Artificial Intelligence (AI) algorithms for personalization and game adaptation

ECTS credits : 2

Course description :

Course description to be added soon.

Objectives :

The course articulates definition, theory about Sensorimotor, Cognitive and Functional levels and hands-on with hardware devices and software. We will detail then immersion in regards to these levels and we will focus then on the increasing use of gestures in Augmented Reality and Virtual Reality to interact. The final goal is to give the students the autonomy to understand the constraints and the possibilities of these technologies and open the discussion about how they take part to build the metaverse.

ECTS credits : 2

Course description :

This module introduces the main methods for designing movement-to-sound interactive systems. The course is comprised of theoretical and methodological contents, as well as practice-based projects equating with realistic use cases. The course will cover the main concepts of movement-based interaction design, as well as the technical bases of movement analysis and sonification (sensors, signal processing, motion analysis, gesture recognition, sound synthesis). These notions will then be further developed in group projects based on actual cases of movement-based interactive systems.

Objectives :

On completion of the course, the student should be able to :

• learn to design movement-based interactive systems in a participatory way
• identify appropriate motion capture solutions
• understand the technical bases of movement signal processing and gesture recognition as well as their context of application
• use tools for motion analysis, sound synthesis and mapping
• learn to apply knowledge in designing an actual case study and implement an interactive system linking human movement and sonification

ECTS credits : 2

Remarkably, due to the rapid proliferation of wearable devices and smartphones, the Internet of Things (IoT)-enabled technology is evolving with regard to healthcare from providing conventional hub-based system to more personalized healthcare system (PHS). The successful utilization of IoT enabled technology in PHS will facilitate faster and safer preventive care, lower overall cost, improved patient-centered practice and enhanced sustainability. Future IoT-enabled PHSs will be realized by providing highly customized access to rich medical information and efficient clinical decision-making to each individual with unobtrusive and successive sensing and monitoring. This module will focus on: a) the state-of-the-art research and applications in utilizing IoT-enabled technology for healthcare systems, b) analysis of efficient scientific and engineering solutions, c) address of the needs and challenges for integration with new technologies, and d) provision of visions for future research and development in the area via novel smart sensing technologies, IoT architectures, services, applications, and AI-based data analytics for PHS and applications.

Objectives :

The objectives will be added soon.

ECTS credits : 2 

Course description :

One of the very promising fields of robotics and interactive systems application is the human learning and skills acquisition. This course will first of all introduce how do humans acquire motor skills by explaining the basics of the sensorimotor learning principles (Piaget Theory etc.) as well as the cognitive mechanisms that are activated permitting to integrate and constantly update the sensorimotor input. Intrinsic, extrinsic and exteroceptive feedback received from learner’s body, his master and the environment in general play a crucial role during the learning process. AI and related technologies have the power to artificially generate this feedback by augmenting the reality and creating a new source of information that comes to support learning. For the activation of this augmented feedback it is essential to identify and understand all the gestural know-how components, to model and analyze them. Students will thus discover different feedback mechanisms as well as feedback typologies that can be provided in order to guide the learner and permit him to adjust his gestural errors. The principles for the design of efficient augmented feedback will be presented. Students will also discover concrete examples of augmented feedback implementation in various fields (sports, rehabilitation, arts etc) through a flipped classroom. They will also have the possibility to design and develop their own mechanism and feedback for a posture’s ergonomy monitoring during a technical work session.

Objectives :

– familiarity with the basics of learning theories
– model and analyse gestural data
– present a methodology for the design of interactive systems for know-how transmission based on sensorimotor learning principles

ECTS credits : 2

Course description :

Course description to be added soon.

Objectives :

The objectives will be added soon.

ECTS credits : 2 

Course description :

This course introduces students to the fundamental engineering, computational, and experimental methods in human-robot collaboration (HRC), with an emphasis on artificial intelligence and robot reasoning. Additionally, students will learn how to develop and analyze experiments to evaluate HRC frameworks.

Students will be first introduced to the concept of human-robot collaboration and its real-world application areas (industry, healthcare, personal assistants, and human learning). Then, the student will be given tools and methods for analyzing the workstation design and robot application in terms of productivity, flexibility, safety, and ergonomics. The lectures will cover the following key concepts:
• Artificial intelligence approaches for human-robot interaction: Human pose estimation (Open Pose), Gesture recognition (3D and 2D convolutional neural networks), Object detection (semantic & instance segmentation – Yolo, Mask RCNN, Vision Transformers), Reinforcement Learning (value-based, policy-based and model-based learning), and Robot Learning from Demonstration (LfD).
• Robotics: Kinematics (forward & inverse kinematics and Jacobian matrix), Robot Operating System (ROS), and Analysis of robots’ embedded sensors (RGB camera and Torque sensors).
• Design of the workstation: Existing safety regulations (ISO) for collaborative robotics and Ergonomic analysis approaches.
Throughout the course, it will be discussed recent papers on HRC and their applied methods and techniques. Students will present papers in class and work in teams on an HRC research project development and evaluation.

Objectives :

The objectives will be added soon.

ECTS credits : 2

Course description :

The module focuses on developing models of human intelligence in the context of creative activities, where such models can be implemented, tested, and further refined via computational and robotic (embodied) systems. By creative activities, we understand those displayed in the visual arts, music performance, dance, crafts and related human activities. The body, its use in a creative activity, such as drawing, provides constraints which are not naturally taken into account with a traditional computational approach. The body and its uses will impact on the engineering of design system. It will also prescribe what movements are likely and desirable and those which are not. It impacts on the strategies put in place: e.g. one draws using a limb, with a hand, which may get in the way of what is visible.

The course will take a multidisciplinary approach. We will consider how robotics systems can be built by combining a deeper understanding from: the psychology of visual perception, human movement science (such as found in graphonomics), the psychology of visual art, AI for creative systems and artistic support, recent progress with Neural networks (such as Deep Learning approaches used to isolate and transfer painting styles). Quality evaluation by robotics systems will be introduced: how can a robot be able to measure the quality of an artefact either as it is produced or once completed; for example, what is the influence of movements used to create a drawing or painting. In the course, we will consider a number of philosophy of AI questions, such as: How to make a robotic system a useful collaborator to the human artist? What other applications, e.g. in training and education are possible? What is the impact of movement mimicry for the social acceptance of robots? The state of the art at the intersection of AI, robotics, creative and artistic applications will be presented.

Course description :

Course description to be added soon

Objectives :

The objectives will be added soon.

ECTS credits : 2 

When people think of machines they often picture devices which perform intricate tasks in order to achieve certain goals. Moreover, computational thinking and computational implementations are linked with specific processes that achieve these goals. On the other hand, when people are asked about creativity they think of this out-worldly, almost divine intervention moment that takes place in a person’s mind and breeds new ideas out of thin air. Creativity and computational thinking therefore seem like two unbreachable sides of the human intellect that belong to distant realms. But is that so? Through the research of AI computer scientists, cognitive scientists and psychologist we have discovered that we can gain an insight of how human creativity works by analyzing some processes that it entails through a computational lens. Inspired by this research, in this class we will attempt to bridge these two realms and introduce a methodology that focuses on the creative process from a computational point of view. Our ultimate goal will be to learn how to design and built robots that channel and unleash human creativity and may even be creative in their own right.

The methodology that we will use in this class is based on a particular branch of human creative processes, Crafts. Crafts are widely considered as creative processes however there seems to be a paradox concerning whether they are truly creative. Margaret Boden a research professor of cognitive science defines creativity as the ability to come up with ideas or artifacts that are new, surprising and valuable. However, in the case of crafts it is evident that they shouldn’t qualify as creative given this definition. That is because crafts are structured processes in which the person follows a particular set of instructions to produce a known result. How are crafts creative then? Crafts follow a different creative approach than the common portrayal of creativity as this divine intervention moment (a top-bottom process). Instead they are a bottom-up process. In crafts the process is laid out explicitly in the form of instructions- an algorithm. The end result of this algorithm is known and it is in no way surprising or new. The creative focus of crafts is not the end result per se, rather than the process itself. The person uses this process as a generative system. By changing parts of the process, introducing new sub-processes or removing/altering them, they alter the end product in unexpected ways. And this is where creativity kicks in. The person is not concerned with achieving a certain result rather than experimenting with the algorithm in creative ways.

This is the framework we will follow in this class in order to design machines that can perform creative processes and enhance our own creative thinking. Based on this framework I have created a robotic platform, the dotBot, that can help us understand and experiment with computational creative processes. The dotBot is a robot that can create images made of pixels on vertical surfaces. It consists of two main robotic parts the Base and the Cartridge. The Base moves the Cartridge in discrete points of an imaginary grid on a vertical surface. The Cartridge is responsible for rendering a pixel in each particular point. Furthermore, the Cartridge is detachable and thus, can be exchanged with other Cartridges. The way the Cartridge is designed defines how pixels are going to be rendered and therefore defines the creative style of the drawing. This tool (dotBot) was created as an analogy to crafts. The Base and its function represent the standard structured process of a craft and the Cartridge represents the additional myriad sub-processes that we can introduce to experiment with that process.

The goal of this class is to use this theoretical framework and the dotBot tool to allow students to learn how to design and built robotic parts that can produce creative outputs. Apart from the theoretical background that will be introduced, this class will focus on providing students with a wide range of technical skills concerning the design of a creative robot, such as, Arduino, Digital Fabrication, Electronics Design, Mechanical Design and 3d Modeling Tools. The students will be able to have a hands-on experimentation with the dotBot robotic platform. Moreover, through the skills they will acquire, they will be called to design and assemble a functioning robotic extension (Cartridge) for the platform. The class will include workshop sessions in which the students will develop their ideas and build their robots. In these sessions I will constantly provide the appropriate guidance and encourage the fruitful exchange of ideas between students. At the end of the class they will be called to demonstrate their robotic part and produce a creative piece which will remain in the university’s premises.

Course description to be added soon.

Course description to be added soon.

Course description :

The Summer School aims to bring together professionals of Artificial Intelligence, robotics and interactive systems, to reinforce exchanges between industries, scientists, industrial factors and students in a more pedagogical perspective. The curriculum is structured in the form of industrial visits, workshops, conferences and presentations with specific learning objectives.

Objectives :

– discover scientific methods and tools used for interaction in the field of AI and robotics
– learn how to design digital interfaces for human-machine interaction, using motion capture technologies
– develop a creative and innovative approach in the design and deployment of interactive systems

ECTS credits : 2 

Course description :

Course description to be added soon.

Objectives :

The objectives will be added soon.

ECTS credits : 2 

Course description :

Course description to be added soon.

Objectives :

The objectives will be added soon.

ECTS credits : 2 

Course description :

Cars are incorporating more and more ‘intelligent’ driver assistance functions, and the first ‘autonomous’ vehicles (which means that can autopilot without a driver) will soon make their appearance on the market. All this is made possible in particular by progress with real-time intelligent analysis of videos. The purpose of this course is to present the specific issues in the field of intelligent vehicles, and to provide an overview of the various types of AI that they use, especially those that allow real-time ‘understanding’ of visual scenes.

Objectives :

By the end of the module, student :

• Will have an overall picture of what the intelligent vehicle is, as well as the domain issues
• Will haved acquired technical expertise in terms of the application of AI on smart vehicles
• Will have reached a better understanding of the challenges of the sector and will be able to propose concrete and innovative solutions, based on AI

ECTS credits : 3

Course description :

Machines are exceptionally effective in performing intricate tasks and achieving specific goals. But what happens when a machine’s goal is creativity. How can we design machines that channel and unleash human creativity or even be creative in their own right? A way to understand how to implement creative processes in a machine is to explore human creative processes. In fact there are human creative processes that are structured enough to be understood as a set of instructions, an algorithm. These processes are human crafts. Crafts combine a high technical skill with fixed rules but which leave enough space for creative tweaking. Crafts are traditionally taught from person to person and are perfected through personal talent and experience. Even though many people that perform crafts might not be aware, unconsciously they follow a common set of tasks. Therefore, by exploring and understanding these underlying instructions we can create algorithms that describe the processes. We can use these algorithms to create machines that mimic the human performance of the craft. Finally, we can extrapolate these models and create our own algorithms that perform creative tasks.

Objectives :

In this class the students will learn how to design creative processes that can be implemented by creative machines through analyzing the way human crafts are performed. They will be able to analyze a range of examples of creative processes and be inspired by them. The students will acquire a solid understanding of how a creative goal can be translated into a set of instructions and finally to software and hardware. This understanding will be further enhanced by familiarizing themselves with the operation of an actual creative machine, the dotBot along with the halftone Cartridge. Through this exploration the link between a creative goal and the final implementation will be clarified. Finally, the students will have the opportunity to use this newly acquired knowledge to experiment, test their skills and design their own versions of creative machines.

ECTS credits : 2

Course description :

The body and gesture play a crucial role in all human activity. From an early age humans develop sensory and motor skills through their interaction with the surrounding environment. We accumulate these experiences and enrich our abilities. Our body is transformed, allowing us to express ourselves, communicate and interact with others and with our environment. New technologies contribute to a deeper  understanding of this interaction, through motion capture, gesture modeling, machine learning and analysis of artistic but also of technical gestures. This event aims to bring together gesture professionals, scientists, artists, ergonomists etc., to discuss the potential of synergies between art and industry. The goal is to identify the new uses of motion capture and ‘embodiment’ in creative and industrial processes. During the think-tank perspectives and suggestions for long-term research around human gesture will defined. Proceedings of the collegial discussions and the exchanges which take place will be drafted.

Objectives :

• Students should emerge with a global picture of issues related to AI and movement in industry and art
• Meetings and exchanges between students and professionals/scientists in the field will be encouraged
• Development of students’ critical thinking skills

ECTS credits : 1

Course description :

Like the Think-Tank, the Summer School aims to bring together professionals of Artificial Intelligence and Movement field, to reinforce exchanges between industries, scientists, artists and students but in a more pedagogical perspective. The curriculum is structured in the form of courses with specific learning objectives. The theoretical themes adressed in the MS will be completed here by technical aspects. For example, students will be able to experiment with software and algorithms for data processing, gesture recognition, creation of interactive interfaces and VR / AR.  Unlike the Think-Tank which aims stimulate global thinking around AI, the summer school has a more applied and concrete objective, giving them the possibility to develop mini-projects or a group project and present them at the end of the summer school.

Objectives :

• Discover scientific methods and tools used for interaction in the field of AI and movement
• Learn how to design digital interfaces using motion capture and gesture recognition technologies
• Develop a creative and innovative approach in the design and deployment of interactive systems

ECTS credits : 3

Course description :

The Group Project is a collaborative work among the students of AIMove, accordingly to the subjects of Factory of the Future, Intelligent Vehicles and Cultural and Creative industries. At the end of the first 6 months, each student has to deliver a professional thesis. It is a written document that links research work with theoretical and practical knowledge. More specifically, this work will be the result of a collective project in which one or more groups of students work on a given subject, according to the industrial sector to which they wish to specialize, implementing the skills acquired while the different modules. This collective work will lead to a professional thesis based on the scientific and technical contribution to the collective project. In addition, the format of the professional thesis will follow the model of a scientific paper and its submission to a scientific conference will be highly recommended.