Master ISICG - parcours Computer Graphics

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (6h)

Tutorials (12h)

Practices (12h)

Pre-requisites:

in progress

Objectives:

This module presents an approach to the large families of algorithms commonly used to efficiently solve complex computer science problems.

The aim of this module is to provide a set of concrete algorithmic tools to enable you to provide interesting solutions to various common problems in computer science.

Learning outcomes:

in progress

Indicative contents:

This course will focus on the notions of Divide and Conquer, Dynamic Programming, Greedy Algorithms, and Branch and Bound (separation and evaluation).

Methods of assessment:

Written test, project

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (9h)
Tutorials (9h)
Practicals (12h)

Pre-requisites:

Basic notions of algorithms.

Objectives:

This course offers a solid foundation on the different artificial intelligence methods applied in areas as varied as problem solving in large search spaces, the resolution of constraint satisfaction systems or uncertain reasoning.

Learning outcomes:

This module will allow students to master these various AI methods and algorithms and will be able to put them into practice in real situations that they will work on during the Practical Work sessions.

Indicative contents:

• Search methods with heuristics in a state space;
• Constraint satisfaction problems (CSP);
• Algorithms for games;
• Knowledge representation and expert systems;
• Learning techniques;
• Introduction to neural networks.

Methods of assessment:

Written test

Suggested bibliography:

• Intelligence artificielle Stuart Russel et Peter Norvig Edition Perason, (2010)
• Mooc (sur Youtube) : Cours Intelligence artificielle par Hugo Larochelle (Quebec Canada): https://www.youtube.com/watchv=stuU2TK3t0Q&list=PL6Xpj9I5qXYGhsvMWM53ZLfwUInzvYWsm

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (12h)
Tutorials (18h)

Pre-requisites:

in progress

Objectives:

Parallel programming on graphics card (GPU): Introduction to massively parallel programmable graphics processors

• Concepts of threads and cores
• Local, global and shared memory
• Memory vs supercomputing
• CUDA Libraries
• Illustration on linear algebra, image processing and factorization problems

Learning outcomes:

in progress

Indicative contents:

in progress

Methods of assessment:

Written test, project

Suggested bibliography:

in progress

Credits: 6
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (22.5h)
Tutorials (16.5h)
Practicals (21h)

Pre-requisites:

in progress

Objectives:

Master the fundamental methods of computer graphics.

Learning outcomes:

in progress

Indicative contents:

in progress

Methods of assessment:

Written test, practical work

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (18h)
Practices (12h)

Pre-requisites:

None

Objectives:

The aim of this course is to acquire basic knowledge and tools for image processing.

Skills acquired at the end of this module:

• knowledge of digital image processing tools
• image filtering
• edge detection
• segmentation
• image matching

Learning outcomes:

At the end of this module, the students have a large panel of basic skills related to image processing, skills that can be used to tackle real-world applications directly (biomedical image segmentation, photo restoration, morphology-based animation or tool movement planning) and can also be used as background skills in eg. computer vision.

Indicative contents:

This module is an introduction to digital image processing. It focuses on low-level processing tools, addressing:

• preprocessing: histogram-based operations (equalization, thresholding)
• filtering (convolution)
• mathematical morphology
• segmentation
• edge detection.
• image matching (keypoint detection, keypoint matching, homography)

Methods of assessment:

Written test, project

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (12h)

Tutorials (9h)

Practices (9h)

Pre-requisites:

Basic numerical linear algebra and differential calculus. The software OCTAVE will be used for the numerical computations. Note that MATLAB can be equivalently used. Examples and solutions will be given with the OCTAVE/MATLAB syntax. The modeling language AMPL will be installed on your computer during the first week. You will be given a free trial license to install this software.

Objectives:

Learn to solve an optimisation problem numerically.

Learning outcomes:

in progress

Indicative contents:

The goal is to learn how to effectively solve an optimization problem. We will see how to modelize an optimization problem and to compute an optimal solution in some concrete situations. We will basically work on optimality conditions of an optimization problem. We will recall the optimality conditions and how to numerically solve them. Outline of the course: Review on some tools from linear algebra, differential calculus and convexity – Warm-up with Matlab/Octave – Introduction to optimization – Modeling languages – First steps with AMPL – Unconstrained optimization – Least-squares problems – Linear programming- Nonlinear programming.

Methods of assessment:

Written test, report

Suggested bibliography:

Fourer, R., Gay, D.M., Kernighan, B.W.: AMPL: A Modeling Language for Mathematical Program- ming, 2nd edn. Brooks/Cole, Pacific Grove (2002)

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (9h)

Practices (21h)

Pre-requisites:

in progress

Objectives:

in progress

Learning outcomes:

in progress

Indicative contents:

Real-time rendering:

• Rasterization, OpenGL pipeline
• Shader programming
• Camera management
• Simple local lighting
• Textures
• Deferred shading and post-processing

Methods of assessment:

Project

Suggested bibliography:

en cours

Credits:3
Language:

French

Course mode:

On-site

Methods of delivery:

Tutorials (39h)

Pre-requisites:

None

Objectives:

Part 1 Communication : This module is designed to help students apply for internships. It equips them with methodological tools and enables them to understand the challenges and stages of recruitment. In addition, students reinforce their oral fluency through a number of exercises: a 180-second Elevator pitch; a critical analysis of a socio-technical controversy in its polemical and media dimensions, combined with a presentation of the players involved and the arguments associated with the different positions. The aim is to develop convincing and adaptable skills. Team-building exercises are designed to get students to work together and put them in a collective interview situation. – Pay attention to posture and body language – Express yourself with ease – Synthesize – Analyze documents and identify arguments – Present a project, justifying the choices made

Part 2 Management : This module aims to make students think about the issues facing a company, how the right strategy is determined, using methodological tools, and to identify interested parties and their performance management.

Learning outcomes:

• Develop your human and relational qualities
• Communicate in writing, orally, in several languages
• Work as a team, self-assess (strengths and weaknesses)
• Develop your abilities to enter professional life
• Demonstrate cultural openness, be curious, have a critical mind
• Work on your dynamism, be capable of commitment, leadership
• Know how to integrate business and societal issues in an international context
• Know and understand the business world
• Manage projects

Indicative contents:

Part 1 Communication : Job interview simulations (individual and collective) are offered as well as the creation of the key elements of a file, namely the CV, cover letter, LinkedIn profile, online applications, etc. A current review (scientific and technical news) is produced at each tutorial as well as a final presentation on a subject related to the professional world. This requires documentary research and preparation of the speech as well as the visual support used for the defense. Work on argumentation and the rhetorical aspects of speech is presented. Students approach a socio-technical controversy by identifying the various positions and issues at stake in the debate, particularly in its media dimension. They report on their documentary research and the choices they have made to address the controversy in an oral presentation.

Part 2 Management :

Chap 1. The company and its environment

• The company
• Analysis of its environment, its market
• The choice of a strategy thanks to a good diagnosis
• React to changes in the environment

Chap 2. The company and its strategic choices

• Notions – strategy, organizational policy, competitive advantage, the different levels of strategy
• The 3 strategies resulting from Porter methods
• Growth strategies * Innovation * Entrepreneurial and managerial logic
• The purpose of a company

Chap 3. Company performance.

• Company management and performance
• Identify stakeholders and their objectives
• Concept- governance, management, performance, decision-makers

Methods of assessment:

Written test, oral, presentation

Suggested bibliography:

• Perez D., CV, lettre de motivation, entretien d’embauche, L’Étudiant, Ed. Paris, 2014, 416 pages.
• Engrand S., Projet professionnel gagnant ! Une méthode innovante pour cibler stages et premier emploi, Dunod, Ed. Paris, 2014, 180 pages.
• Davidenkoff E., Le guide des entreprises qui recrutent : hors-série 2015 : faire la différence en entretien, négocier son premier salaire, débuter à l’étranger, L’Étudiant, Ed. Paris, 2015
• Charline Licette, Savoir parler en public, Studyrama Pro, 2018
• Fabrice Carlier, Réussir ma première prise de parole en public, StudyramaPro, 2018
• Cyril Gely, Savoir improviser : l’art de s’exprimer sans préparation, Groupe Studyrama-Vocatis, 2010
• Lelli A., 2003, Les écrits professionnels : la méthode des 7C – Soyez correct, clair, concis, courtois, convivial, convaincant, compétent, Dunod, Ed. Paris, 2003, 168 pages.

Credits: 6
Language:

French/English

Course mode:

On-site

Pre-requisites:

None

Objectives:

Consolidation of the experience acquired during training within a research laboratory.

Indicative contents:

Depending on the topic of the laboratory work.

Learning outcomes:

• Integrate into and within a work team
• Show initiative
• Test your curiosity
• Structure your ideas and the stages of their implementation
• Demonstrate scientific rigor
• Learn to meet deadlines
• Know the safety rules in force within the structure

Methods of assessment:

Report, evaluation sheet (lab behavior), oral presentation

Suggested bibliography:

Depending on the topic of the laboratory work.

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (9h)

Tutorials (9h)

Practices (12h)

Pre-requisites:

Basic notions of algorithms.

Objectives:

The objective of this course is to allow students to master the fundamental concepts of machine learning and to apply these notions to certain concrete problems such as information security. Then to make them acquire knowledge on supervised and unsupervised learning techniques.

Learning outcomes:

At the end of this activity, the student will have acquired knowledge on:

• Allow students to master the fundamental concepts of machine learning and apply these concepts to real-world problems.
• Help them acquire knowledge of supervised and unsupervised learning techniques.

Indicative contents:

• Expert system generators;
• Genetic algorithms;
• Introduction to Bayesian networks;
• Distributed artificial intelligence: Introduction to multi-agent systems;
• Intelligent systems based on ant colonies

Methods of assessment:

Written test, project

Suggested bibliography:

Apprentissage artificiel, concepts et algorithmes, Eyrolles.

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (9h)

Practices (21h)

Pre-requisites:

• Introduction to Digital Image Processing
• Artifical Intelligence 1 (neuronal networks, back-propagation, perceptron, multi-layer perception)

Objectives:

This course mainly focuses on computer vision algorithms (image-to-category) and image processing using image-to-image networks (image segmentation and A.I.-based image generation).

The building blocks of this module are:

• Convolutional Neural Networks
• Classification and segmentation using convolutional networks
• Autoencoders, adversarial and variational networks
• Loss functions

Learning outcomes:

Neuronal networks are studied and presented from both a mathematical and programmatical standpoint, in the context of both computer vision and generative A.I. (image-to-image). The shape and design of CNNs is justified and explained. The role of several loss functions is discussed in various applications and scenarios (classification, segmentation…)

Overfitting and underfitting is discussed, so are ways to deal with these issues.

Indicative contents:

This course introduces the concept of image classification and segmentation and explains why these features are best addressed by A.I. methods compared to other methods (including some methods already introduced in the Introduction to Digital Image Processing module).

Practical work ranges from simple classification, image denoising, A.I.-based upscaling to motion-based video interpolation.

Methods of assessment:

Written test, project

Suggested bibliography:

• Generative Adversarial Networks, Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Alec Radford, Luke Metz, Soumith Chintala, 2014
• Auto-Encoding Variational Bayes, Diederik P Kingma, Max Welling, 2014
• Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (9h)

Tutorials (9h)

Practices (12h)

Pre-requisites:

in progress

Objectives:

in progress

Learning outcomes:

in progress

Indicative contents:

Introduction to game theory:

• Solution Concepts
• Games in normal form: dominant/dominated strategies
• Nash equilibrium
• Pareto criterion
• Mixed strategies and zero-sum games
• Extensive form games: correlated equilibria
• Repeated games

Methods of assessment:

Written test, project

Suggested bibliography:

in progress

Credits: 6
Language:

English

Course mode:

On-site/Online

Methods of delivery:

Lectures (18h)

Tutorials (18h)

Practices (24h)

Pre-requisites:

Computer graphics modules of the first semester

Objectives:

This course covers the topic of shading mostly. It introduces the students to many computer graphics topics related to light-object interactions and ray tracing. Ray tracing and differential ray tracing are presented, justified and implemented. Local surface-area bound direct lighting models are examined through Bidirectional Reflectance Distribution Functions (BRDF). Explicit and statistical shading models are discussed, with pros and cons. Indirect lighting and global illumination is introduced through Monte Carlo integration and importance sampling.

Learning outcomes:

• Full understanding of shading and lighting through BRDFs
• Understanding of pixel fingerprints on surfaces and ray differentials
• Whay naïve raytracing does not make sense when it comes to shading
• Understanding of statistical vs explicit formulations of shading models
• Understanding of the challenges related to global illumination
• Applied use of key mathematical concepts such as Cumulative Distribution Functions, with comparisons with alternate methods (such as GANs, studied in the Computer vision module, and MCMC).
• Applied use of Monte Carlo integration through various importance sampling schemes (angle based, NDF/VNDF, BRDF…)

Indicative contents:

This module deep-dives into the microfacet shading model. It justifies its existence, how and why it was created, the different concepts involved in its formulation, what happens when legacy shading models are used. It presents open challenges that remain to be worked on.

Methods of assessment:

Project, written test, practical test

Suggested bibliography:

• Physically Based Rendering: From Theory to Implementation, Third Edition, 2016
• Auteurs : Matt Pharr, Wenzel Jakob, Greg Humpreys
• Physically-based rendering of materials with micro-structure, Xavier Chermain, Ph.D thesis, 2019
• Multi-scale appearance for the realistic and efficient appearance of complex surfaces , Eric Heitz, 2014

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (9h)

Practices (21h)

Pre-requisites:

None

Objectives:

in progress

Learning outcomes:

in progress

Indicative contents:

in progress

Methods of assessment:

Written test, project

Suggested bibliography:

in progress

Credits: 3
Language:

English

Course mode:

On-site

Methods of delivery:

Tutorials (30h)

Pre-requisites:

B1 level required.

Objectives:

To bring students towards the European B2/C1 level. The operational and evaluable objectives of this training are:

• Understand most situations that might be encountered at work or while traveling in a region where English is spoken for example
• Develop oral and written language skills
• International English communication

Learning outcomes:

Acquisition of English language skills (objective B2/C1). International, specialty and professional English (CV, cover letters, etc.)

Indicative contents:

• Written and oral comprehension/production work on authentic specialist or general English documents
• Interactive debates on general themes
• Language lab work (pronunciation, listening, repetition, etc.)
• Professional English (writing cover letters, CV, professional interview) academic (summary of documents, emails, sum-ups, etc.)
• Work on specialization and general English vocabulary.
• Presentation of a specialty presentation

Methods of assessment:

Written test, oral

Credits:3
Language:

French

Course mode:

On-site

Methods of delivery:

Tutorials (39h)

Pre-requisites:

None

Objectives:

Part 1 Communication : This module is designed to help students apply for internships. It equips them with methodological tools and enables them to understand the challenges and stages of recruitment. In addition, students reinforce their oral fluency through a number of exercises: a 180-second Elevator pitch; a critical analysis of a socio-technical controversy in its polemical and media dimensions, combined with a presentation of the players involved and the arguments associated with the different positions. The aim is to develop convincing and adaptable skills. Team-building exercises are designed to get students to work together and put them in a collective interview situation. – Pay attention to posture and body language – Express yourself with ease – Synthesize – Analyze documents and identify arguments – Present a project, justifying the choices made

Part 2 Management : This module aims to make students think about the issues facing a company, how the right strategy is determined, using methodological tools, and to identify interested parties and their performance management.

Learning outcomes:

• Develop your human and relational qualities
• Communicate in writing, orally, in several languages
• Work as a team, self-assess (strengths and weaknesses)
• Develop your abilities to enter professional life
• Demonstrate cultural openness, be curious, have a critical mind
• Work on your dynamism, be capable of commitment, leadership
• Know how to integrate business and societal issues in an international context
• Know and understand the business world
• Manage projects

Indicative contents:

Part 1 Communication : Job interview simulations (individual and collective) are offered as well as the creation of the key elements of a file, namely the CV, cover letter, LinkedIn profile, online applications, etc. A current review (scientific and technical news) is produced at each tutorial as well as a final presentation on a subject related to the professional world. This requires documentary research and preparation of the speech as well as the visual support used for the defense. Work on argumentation and the rhetorical aspects of speech is presented. Students approach a socio-technical controversy by identifying the various positions and issues at stake in the debate, particularly in its media dimension. They report on their documentary research and the choices they have made to address the controversy in an oral presentation.

Part 2 Management :

Chap 1. The company and its environment

• The company
• Analysis of its environment, its market
• The choice of a strategy thanks to a good diagnosis
• React to changes in the environment

Chap 2. The company and its strategic choices

• Notions – strategy, organizational policy, competitive advantage, the different levels of strategy
• The 3 strategies resulting from Porter methods
• Growth strategies * Innovation * Entrepreneurial and managerial logic
• The purpose of a company

Chap 3. Company performance.

• Company management and performance
• Identify stakeholders and their objectives
• Concept- governance, management, performance, decision-makers

Methods of assessment:

Written test, oral, presentation

Suggested bibliography:

• Perez D., CV, lettre de motivation, entretien d’embauche, L’Étudiant, Ed. Paris, 2014, 416 pages.
• Engrand S., Projet professionnel gagnant ! Une méthode innovante pour cibler stages et premier emploi, Dunod, Ed. Paris, 2014, 180 pages.
• Davidenkoff E., Le guide des entreprises qui recrutent : hors-série 2015 : faire la différence en entretien, négocier son premier salaire, débuter à l’étranger, L’Étudiant, Ed. Paris, 2015
• Charline Licette, Savoir parler en public, Studyrama Pro, 2018
• Fabrice Carlier, Réussir ma première prise de parole en public, StudyramaPro, 2018
• Cyril Gely, Savoir improviser : l’art de s’exprimer sans préparation, Groupe Studyrama-Vocatis, 2010
• Lelli A., 2003, Les écrits professionnels : la méthode des 7C – Soyez correct, clair, concis, courtois, convivial, convaincant, compétent, Dunod, Ed. Paris, 2003, 168 pages.

Credits: 6
Language:

French/English

Course mode:

On-site

Pre-requisites:

None

Objectives:

Consolidation of the experience acquired during training within a research laboratory.

Indicative contents:

Depending on the topic of the laboratory work.

Learning outcomes:

• Integrate into and within a work team
• Show initiative
• Test your curiosity
• Structure your ideas and the stages of their implementation
• Demonstrate scientific rigor
• Learn to meet deadlines
• Know the safety rules in force within the structure

Methods of assessment:

Report, evaluation sheet (lab behavior), oral presentation

Suggested bibliography:

Depending on the topic of the laboratory work.

Credits: 3
Language:

French/English

Course mode:

On-site (internship)

Pre-requisites:

None

Objectives:

Discover the world of business or international research work.

Learning outcomes:

Compare the skills acquired during training with the demands of the socio-professional world.

Indicative contents:

At least two months spent within the company (or in an international research laboratory) as an intern.

Methods of assessment:

Report, evaluation sheet, oral presentation

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (15h)

Tutorials (16h)

Pre-requisites:

in progress

Objectives:

in progress

Learning outcomes:

in progress

Indicative contents:

in progress

Methods of assessment:

Project

Suggested bibliography:

in progress

Credits: 3
Language:

English/French

Course mode:

On-site

Methods of delivery:

Tutorials (40h)

Pre-requisites:

Background in Computer Science, Computer Graphics and Maths.

Objectives:

The objectives of this module are to present the professional aspects and economic model of computer graphics, through the realization of mini-projects that take into account the constraints usually encountered in industry.

Learning outcomes:

At the end of this module, students will have acquired a business vision of their field of study, enabling them to prepare for recruitment interviews for internships or permanent positions.

Indicative contents:

The module is illustrated by mini-projects using various technologies or hosting platforms (Steam, Blender, Python, etc.).

Methods of assessment:

Project

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Tutorials (15h)

Practices (15h)

Pre-requisites:

Basics of geometry processing and GPU programming through a graphics pipeline.

Objectives:

Understanding of the key advantages of 3D printing compared to other manufacturing methods (milling, molding…) as well as the limitations.

Converting 3D model files into print paths and finally manufacturing 3D-printed real-life objects, from A to Z.

The course covers common geometric operations in manufacturing, especially relevant in 3D printing. The G-Code programming language is introduced.

Current issues related to additive manufacturing are addressed: the course discusses the limits of the 3D printers, addresses the limitations in manufacturing quality and explains how careful software design makes it possible to work around many of these problems.

Learning outcomes:

• ability to write software programs driving 3D printers (machine control)
• ability to generate 3D model slices using a vector or discrete geometrical approach
• GPU programming applied to 3D printing
• basic knowledge about the state of the art in key areas related to 3D printing, such as printing quality and support structures.

Indicative contents:

The course covers the following points:

• Introduction to additive manufacturing and 3D printing
• Slicing, generation of print paths (perimeters, shells, infllls…)
• Manufacturing quality issues.
• Digital command files (G-Code) and the role of the printer firmware
• Algorithmic modeling applied to manufacturing in vector and discrete settings
• Generation of support structures
• Overview of current research areas in the field of additive manufacturing

Students may test their work or just print objects for fun on a few fused filament 3D printers that are at their disposal.

Methods of assessment:

Project

Suggested bibliography:

• Modeling and Toolpath Generation for Consumer-Level 3D Printing Cours ACM SIGGRAPH, Los Angeles, 2015 H. Quynh Dinh, Sylvain Lefebvre, Filipp Gelman, Frédéric Claux
• Layered manufacturing: current status and future trends. Debasish Dutta, Fritz B Prinz, David Rosen, and Lee Weiss. Journal of Computing and Information Science in Engineering, 1(1):60–71, 2001

Credits: 6
Language:

English

Course mode:

On-site

Methods of delivery:

Lectures (30h)

Tutorials (30h)

Pre-requisites:

in progress

Objectives:

Master the common modeling/animation techniques in realistic image synthesis Topological-based geometric modeling. Point cloud modeling. Particle systems. Differential geometry. Main animation methods. Dynamic simulation. Growth simulation. Free-form deformations. Animations of deformable objects. Mass-spring system. Dynamic simulation. Physics based animation. Mathematical techniques used in animation).

Learning outcomes:

in progress

Indicative contents:

in progress

Methods of assessment:

Written test, practical work

Suggested bibliography:

in progress

Credits: 3
Language:

English

Course mode:

On-site

Methods of delivery:

Lectures (12h)

Tutorials (18h)

Pre-requisites:

in progress

Objectives:

Master the graphic production chain, the composition of images, the production of 3D films and “Motion Design”.

Learning outcomes:

in progress

Indicative contents:

in progress

Methods of assessment:

Project

Suggested bibliography:

in progress

Credits: 6
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (33h)

Tutorials (27h)

Pre-requisites:

in progress

Objectives:

Master the common techniques of realistic rendering in image synthesis (Texture synthesis. Regular and stochastic sampling. Aliasing and antialiasing. Color models. Ray tracing and extensions. Beam casting. Image-based rendering. Real-time rendering. Physical models . Aging of materials. Participating environments. Light-matter interactions).

Learning outcomes:

in progress

Indicative contents:

in progress

Methods of assessment:

Written test, project

Suggested bibliography:

in progress

Credits: 3
Language:

English

Course mode:

On-site

Methods of delivery:

Tutorials (30h)

Pre-requisites:

B1 level required.

Objectives:

To bring students towards the European B2/C1 level. The operational and evaluable objectives of this training are:

• Understand most situations that might be encountered at work or while traveling in a region where English is spoken for example
• Develop oral and written language skills
• International English communication

Learning outcomes:

Acquisition of English language skills (objective B2/C1). International, specialty and professional English (CV, cover letters, etc.)

Indicative contents:

• Written and oral comprehension/production work on authentic specialist or general English documents
• Interactive debates on general themes
• Language lab work (pronunciation, listening, repetition, etc.)
• Professional English (writing cover letters, CV, professional interview) academic (summary of documents, emails, sum-ups, etc.)
• Work on specialization and general English vocabulary.
• Presentation of a specialty presentation

Methods of assessment:

Written test, oral

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (15h)

Tutorials (15h)

Pre-requisites:

in progress

Objectives:

in progress

Learning outcomes:

in progress

Indicative contents:

Discrete geometry:

• Understand pixel/voxel geometry
• Methods for managing it
• Model/use the discrete world in imaging
• Main discretization algorithms
• Analytical models in the discrete world
• Discrete geometry applications

Lighting simulation:

• Physical formalization
• Methods for simulating them
• Goal: build a realistic image

Image-based rendering:

• Idea of using natural light flows
• Principles of the main families of methods
• Acquisition/restitution/interpolation
• Use photographs for realistic rendering

Methods of assessment:

Project

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (12h)

Tutorials (18h)

Pre-requisites:

in progress

Objectives:

Classification of partial differential equations. Finite difference methods. Finite element methods. Typical physical problems. Methods for solving physical problems. Optimization methods in electromagnetism/light.

Learning outcomes:

in progress

Indicative contents:

in progress

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 6
Language:

French/English

Course mode:

On-site/Hybrid

Methods of delivery:

Scientific project (one day/week)

Pre-requisites:

None

Objectives:

Carry a scientific or entrepreunarial project. 3 options:

• continue their « research » project carried out in M1 within the framework of the Cordées de la recherche
• carry out their project within the framework of the « Ateliers de l’innovation » offered by the IAE Limoges
• carry out their project in conjunction with a company, a CRT, a LabCom, etc.

Methods of assessment:

Project

Credits: 24
Language:

French/English

Course mode:

On-site

Methods of delivery:

6 months internship

Pre-requisites:

None

Objectives:

6 months training period in a company or in a research laboratory

Methods of assessment:

Report, oral, evaluation sheet