Tracks and courses of the 3rd year and master 1 at École polytechnique

This webpage gathers information about courses for the 3rd year (which corresponds to master 1 level) in computer science for students at École polytechnique: tracks, courses, etc. It is organized in 10 thematic tracks.

Introduction

Computers are present in all professional activities that students at École polytechnique will have: from management of companies to the development of research or industrial products. In their activities, they will have to use software tools and develop new ones.

The courses offered to third and fourth year students at the Department of Computer Science (master level) aim at enabling students to acquire the knowledge necessary to understand the technological changes in the broad field of science and technologies of information and communication that constitute the landscape in which engineers, civil servants and researchers evolve.

Objectives

The Computer Science Master 1 program (PA Info, or IP Paris Master 1 programs) has a triple mission: to provide the students with the core concepts of computer science, training them at the level of the best universities in the world; to start a specialization which will continue in fourth year either as a engineering-based training or as a research-based training; and to open up the students perspectives by exploring the fruitful interfaces with related disciplines (from other departments). This will provide the students with a high-level expertise to overcome challenges in industry, administration and research.

Prequisites

The generic prerequisites for a registration in PA Info are to have followed at least one computer science course (excluding modal) in 2nd year, and to have validated a computer science project in 2nd year (a modal counts as a project).

Rules

You choice of courses in a computer science track should fulfill the following requirements:

• the mandatory courses have to be taken,
• over each period, at least 4 courses have to be taken,
• a project cannot replace a course, it has to be taken additionally to 4 courses.

IP Paris M1 students

The rules for IP Paris master 1 students are basically the same as above (4 course over each of the two first period), although we are less strict on the mandatory courses.

Future

The Computer Science Master 1 Program leads to the following 2nd years of master, organised in partnership with other institutes, for instance:

• IP Paris masters:
  • Computer Science for Networks
  • Cybersecurity
  • Cyber Physical Systems
  • Data & Artificial Intelligence
  • Foundations in Computer Science (the local implementation of MPRI)
  • High Performance Data Analytics
  • Interaction, Graphic & Design
  • Operations Research
  • Parallel and Distributed Systems
• Université de Paris masters:
  • Master Parisien de Recherche en Informatique (MPRI)
  • Logique Mathematique et Fondements de l’Informatique (LMFI)
  • Mathematique et Informatique appliques a la Cryptologie (MIC)

Contact

In case you have any problem or question, you are strongly advised to ask it on the DIX Slack workspace (please sign in with you @polytechnique.edu email address) or contact Olivier Bournez who is in charge of the 3A at École polytechnique.

Artificial Intelligence and Data Science

Artificial Intelligence is a key area for interdisciplinary science which involves learning sophisticated models from large and heterogeneous sources of data. There are a number of major challenges: big data management in complex, distributed, and dynamic environments; visualizing such data, ensuring its integrity, and dealing with sampling bias; the use of machine learning (including notably deep learning, and reinforcement learning) for constructing models; the deployment and testing of such models for tasks such as exploratory and predictive analytics, and decision making. Such environments abound in both science and industry, and new tools and knowledge are required to meet these challenges.

Future

This track equips the students with a solid background necessary for advanced master 2 studies in the area of artificial intelligence and big data management with a large range of real world applications. Natural choices for master 2 studies include

• the Data and Artificial Intelligence master of IP Paris,
• the High Performance Data Analytics master of IP Paris,
• the Artificial Intelligence & Advanced Visual Computing master at École polytechnique,
• the MVA (Mathématiques, Vision, Apprentissage) master of Paris Saclay University,
• the Data Sciences master of Paris Saclay University.

Other choices abound in prestigious Universities around the world as artificial intelligence and data science are highly active areas of postgraduate and doctoral training.

Rules

In period 1: FMA_51057_EP comes as a complement of CSC_51056_EP and can therefore be selected only if CSC_51056_EP itself has been selected.

Courses

First period:

• Mandatory courses:

  • CSC_51053_EP (was INF553), Friday afternoon
    Database Management Systems
  • CSC_51054_EP (was INF554), Monday afternoon / Wednesday afternoon
    Machine and Deep learning

• Non-mandatory courses:

  • CSC_50P17_EP (was INF517)
    Projet de 3A Science des données
  • CSC_51052_EP (was INF552), Friday morning
    Data Visualization
  • CSC_51056_EP (was INF556), Friday morning
    Topological Data Analysis
  • CSC_51059_EP (was INF559), Wednesday morning
    Architecture des ordinateurs et systèmes d’exploitation
  • CSC_51071_EP (was INF571), Monday morning
    Distributed Data Structures, with a Spotlight on Blockchains
  • APM_51053_EP (was MAP553), Monday
    Foundation of Machine Learning
  • APM_51178_EP (was MAP578), Tuesday afternoon
    Emerging Topics in Machine Learning - Collaborative and Reliable Learning
  • FMA_51057_EP (was MAT557), Wednesday afternoon
    Topologie algébrique

Second period:

• Mandatory courses:

  • CSC_52083_EP (was INF583), Friday afternoon
    Systems for Big Data

• Non-mandatory courses:

  • CSC_52002_EP (was CSC_EP_5a040), Monday afternoon
    Computer Vision: from Fundamentals to Applications
  • CSC_52060_EP (was INF560), Wednesday afternoon
    Algorithmique parallèle et distribuée
  • CSC_52081_EP (was INF581), Wednesday afternoon
    Advanced Machine Learning and Autonomous Agents
  • CSC_52087_EP (was INF581A), Tuesday afternoon
    Advanced Deep Learning
  • CSC_52082_EP (was INF582), Friday morning
    Introduction to Text Mining and NLP
  • APM_52066_EP (was MAP566), Friday
    Statistics in Action
  • APM_52069_EP (was MAP569), Monday
    Regression and classification
  • APM_52188_EP (was MAP588), Tuesday afternoon
    Emerging Topics in Machine Learning - Computational Transport

Project: CSC_50P17_EP

Internship: CSC_52992_EP

Timetable

First period:

Second period:

Contacts

The track coordinators are Ioana Manolescu, Jesse Read.

Cybersecurity

The cybersecurity program is concerned with the security of internet, at large, and with the ever-increasing set of objects from our environment, that it ties together. Past the first era of discovery and the false illusion of new brotherhood-without-the-old-problems-related-to-bad-guys, people are starting to realize that on the Internet, there’re as many bad guys as elsewhere – and also, that there is nothing like a free lunch in securing our communications nowadays.

First, there is no global notion of security. Many problems are security problems: use your laptop and protect your own data; enable and protect the access to Internet; be sure that the communications cannot be eavesdropped, nor altered when you access your bank account; be sure to talk to the right person; and be sure that the communications network infrastructure (such as the Internet, the cell phone system) has not been corrupted and subverted, etc.

Since there is no unique way of accessing this virtual world, there is no unique way to tackle security issues. But what is clear is that one needs to understand how the systems work, how resistant they are, how one would design new and more secure applications. The same is true when you want to connect machines to other machines via networks that interfer with the physical world (old wires, WiFi, etc.) and on which information (packets) live their own life in an open though inpredictible way.

This track is also accessible through the Cybersecurity IP Paris M1.

Courses

First period:

• Mandatory courses:

  • CSC_51057_EP (was INF557), Wednesday afternoon
    From the Internet to the IoT: Fundamentals of Modern Computer Networking
  • CSC_51058_EP (was INF558), Friday morning
    Introduction to Cryptology
  • CSC_51059_EP (was INF559), Wednesday morning
    Architecture des ordinateurs et systèmes d’exploitation

• Non-mandatory courses:

  • CSC_50P20_EP (was INF520)
    Projet de 3A de Cyber-sécurité
  • CSC_51051_EP (was INF551), Tuesday afternoon
    Computational Logic: from Artificial Intelligence to Zero Bugs
  • CSC_51053_EP (was INF553), Friday afternoon
    Database Management Systems
  • CSC_51054_EP (was INF554), Monday afternoon / Wednesday afternoon
    Machine and Deep learning
  • CSC_51063_EP (was INF563), Monday morning
    Introduction to Information Theory
  • CSC_51071_EP (was INF571), Monday morning
    Distributed Data Structures, with a Spotlight on Blockchains
  • APM_51053_EP (was MAP553), Monday
    Foundation of Machine Learning
  • MDC_51005_EP, Friday afternoon
    Introduction to quantum information and computing

Second period:

• Mandatory courses:

  • CSC_52065_EP (was INF565), Monday morning
    Information Systems Security
  • CSC_52068_EP (was INF568), Friday morning
    Advanced Cryptology
  • CSC_52086_EP (was INF586), Tuesday afternoon
    Sécurité des réseaux

• Non-mandatory courses:

  • CSC_52064_EP (was INF564), Monday afternoon
    Compilation

Project: CSC_50P20_EP

Internship: CSC_52991_EP

Timetable

First period:

Second period:

Contacts

The track coordinator is François Morain.

Design of Intelligent Autonomous Systems

The Design of Intelligent Autonomous Systems track gives the theoretical basis for developing embedded and cyberphysical systems. These systems are blooming, and are already present in all aspects of our daily life: smart-apps on smartphones, primary flight computers (on airplanes); autonomous vehicles (Tesla, Google etc.) and drones, possibly organized in swarms; the smart-grid, among other exciting recent applications. There is no system nowadays, that can be designed, specified, developed, simulated nor verified without computer science through its most fundamental concepts: domain-specific langages, specification langages, formal methods, networks, distributed systems, learning etc. Jobs in these domains range from development to research (there are difficult theoretical problems in these fields), from engineering to project management in big firms or in startup or hi-tech companies.

This track is also accessible through the Cyber-Physical System IP Paris Master 1.

Future

This tracks has, as natural follow up in 4A:

• most of the computer science tracks of IP Paris, in particular the cyber-physical track,
• most masters in computer science, embedded systems, systems, control, robotics etc., or cyberphysical systems, for example, Master of Computer Science at TUM, CMU, Berkeley, EPFL, ETHZ, KTH Embedded Systems, Systems Controls and Robotics, ETHZ Robotics, Systems and Control, University of California at Irvine or Delft cyberphysical systems etc.

Partnerships

This program is partially supported by the academic chair Engineering of Complex Systems, in partnership between École polytechnique, Télécom ParisTech, ENSTA ParisTech, and Thalès-DassaultAviation-DCNS-DGA. The chair can offer a certain number of grants for the best internship projects to go to other international institutions (some students went to CMU, GeorgiaTech, Berkely, Austin the last few years).

Courses

First period:

• Mandatory courses:

  • CSC_51057_EP (was INF557), Wednesday afternoon
    From the Internet to the IoT: Fundamentals of Modern Computer Networking
  • CSC_51059_EP (was INF559), Wednesday morning
    Architecture des ordinateurs et systèmes d’exploitation
  • CSC_51075_EP (was INF575), Tuesday afternoon
    Safe Intelligent Systems

• Non-mandatory courses:

  • CSC_50P10_EP (was INF510)
    Projet de 3A Conception de systèmes autonomes intelligents
  • CSC_51051_EP (was INF551), Tuesday afternoon
    Computational Logic: from Artificial Intelligence to Zero Bugs
  • CSC_51054_EP (was INF554), Monday afternoon / Wednesday afternoon
    Machine and Deep learning
  • CSC_51058_EP (was INF558), Friday morning
    Introduction to Cryptology
  • CSC_51063_EP (was INF563), Monday morning
    Introduction to Information Theory
  • CSC_51071_EP (was INF571), Monday morning
    Distributed Data Structures, with a Spotlight on Blockchains
  • PHY_51059_EP (was PHY559A), Monday
    Conception of numerical and analog integrated circtuis

Second period:

• Mandatory courses:

  • CSC_52064_EP (was INF564), Monday afternoon
    Compilation

• Non-mandatory courses:

  • CSC_52060_EP (was INF560), Wednesday afternoon
    Algorithmique parallèle et distribuée
  • CSC_52065_EP (was INF565), Monday morning
    Information Systems Security
  • CSC_52067_EP (was INF567), Wednesday morning
    Wireless Networks: from Cellular to Connected Objects
  • CSC_52068_EP (was INF568), Friday morning
    Advanced Cryptology
  • CSC_52080_EP (was INF580), Friday afternoon
    Large scale mathematical optimization
  • CSC_52081_EP (was INF581), Wednesday afternoon
    Advanced Machine Learning and Autonomous Agents
  • CSC_52086_EP (was INF586), Tuesday afternoon
    Sécurité des réseaux

Project: CSC_50P10_EP

Internship: CSC_52991_EP

Timetable

First period:

Second period:

Contacts

The track coordinators are Éric Goubault, Sylvie Putot.

Efficient Algorithms and Foundations of Programming Languages

This track allows students with a broad interest for computer science to pursue a balanced curriculum between two cardinal aims of the discipline, efficiency and safety. A strong emphasis is put here on theory and mathematical rigor but without loosing the contact with the practical purposes: solving complex tasks on computers in an efficient and reliable way!

This track is also accessible through the Foundations of Computer Science IP Paris Master 1.

This program also focuses on the design of efficient and robust algorithms, making use of advanced algorithmic techniques, and involving mainly two domains: image processing / computer vision and optimization / operational research.

This is by this possibly a multidisciplinary program, at the interface of several domains including algorithms, image processing, computer vision, optimization, computer graphics, computational geometry and randomized methods.

On the one hand, the goal is to generate curiosity and interest for the theoretical aspects of these domains (complexity of algorithms, parallel / distributed paradigms, …); on the other hand, this track aims to introduce students to a wide variety of problems and algorithmic solutions, ranging from experimental results to theoretical tools, showing their practical interest and efficiency through real-world applications

Future

This track coincides with the master 1 part of the Master Foundations of Computer Science of IP Paris, and naturally leads to the master 2 part of the same master, which is the

• Parisian Master of Research in Computer Science

also known as MPRI (Master Parisien de Recherche en Informatique). It is a joined program of Université de Paris, Université Paris Saclay, École Normale de Paris and IP Paris. Students of this master 1 track also enrolled or were accepted in masters abroad, such as MIT, CMU, NYU, Oxford, Imperial, etc.

The track naturally leads also to the following master 2 programs (which are organized in partnership with other institutions):

• Master Mathematiques / Vision / Apprentissage (MVA)
• Operations Research IP Paris master
• Other Masters programs abroad are also possible: EPFL, ETH, Stanford, Carnegie Mellon, Berkeley, McGill, …

Regarding the research internships in 3A, this track offers multiple possibilities both in academia and in industry. A non-exhaustive list of internships done by students in the previous years includes: Ubisoft, Dassault Systèmes, Facebook, Adobe, Apple, Google, Parrot, Disney, Lucas Films, EADS, Thalès, Siemens, CEA, INRIA, …

Courses

First period:

• Mandatory courses:

  • CSC_51050_EP (was INF550), Wednesday afternoon
    Algorithmique avancée

• Non-mandatory courses:

  • CSC_50P13_EP (was INF513)
    Projet de 3A Algorithmes et fondements des languages de programmation
  • CSC_51051_EP (was INF551), Tuesday afternoon
    Computational Logic: from Artificial Intelligence to Zero Bugs
  • CSC_51058_EP (was INF558), Friday morning
    Introduction to Cryptology
  • CSC_51063_EP (was INF563), Monday morning
    Introduction to Information Theory
  • CSC_51071_EP (was INF571), Monday morning
    Distributed Data Structures, with a Spotlight on Blockchains
  • CSC_51073_EP (was INF573), Wednesday morning
    Image Analysis and Computer Vision
  • CSC_51074_EP (was INF574), Tuesday afternoon
    Digital Representations and Analysis of Shapes
  • CSC_51075_EP (was INF575), Tuesday afternoon
    Safe Intelligent Systems
  • APM_51057_EP (was MAP557), Friday
    Recherche opérationnelle : aspects mathématiques et applications
  • MDC_51005_EP, Friday afternoon
    Introduction to quantum information and computing

Second period:

• Mandatory courses:

• Non-mandatory courses:

  • CSC_52060_EP (was INF560), Wednesday afternoon
    Algorithmique parallèle et distribuée
  • CSC_52061_EP (was INF561), Tuesday afternoon
    Randomization in Computer Science: Games, Networks, Epidemic and Evolutionary Algorithms
  • CSC_52062_EP (was INF562), Monday morning
    Géométrie algorithmique : de la théorie aux applications
  • CSC_52064_EP (was INF564), Monday afternoon
    Compilation
  • CSC_52068_EP (was INF568), Friday morning
    Advanced Cryptology
  • CSC_52080_EP (was INF580), Friday afternoon
    Large scale mathematical optimization
  • CSC_52084_EP (was INF584), Wednesday morning
    Synthèse d’Image
  • CSC_52089_EP (was INF589), Monday afternoon
    Computational analysis of high-throughtput sequencing date

Project: CSC_50P13_EP

Internship: CSC_52991_EP

Timetable

First period:

Second period:

Contacts

The track coordinators are Luca Castelli Aleardi, Gilles Schaeffer.

Image, Vision and Machine Learning

This track, proposed jointly with the Graduate Program in Artificial Intelligence & Advanced Visual Computing revolves around the following two main themes: computer graphics and computer vision, on the one hand, and artificial intelligence, on the other, while putting special emphasis on learning-based techniques, in general. Image processing, or more generally analysis, processing and synthesis of visual data in 2D and 3D are now at the heart of many real-world applications. From the entertainment industry (gaming, special effects, virtual and augmented reality) to applications in analyzing large amounts of visual data (scene understanding, robotics, drones, medical imaging, computer-aided design, social networks, etc.), it is now difficult to imagine communication and reasoning in the digital age without visual data.

The main notions presented in this track include: the basics of analyzing 2D images at the pixel level; computer vision and recent techniques based on convolutional neural networks; Computer Rendering on modern graphics hardware; advanced analysis of 3D shapes and their collections, as well as deformation and animation of 3D models; classical and recent techniques in artificial intelligence. All of these topics aim at providing the basis necessary for addressing broad current scientific challenges, which include seamless integration between synthetic and real data, development of “intelligent” tools for vision and graphics; and real-time interaction with 3D data, among many others. Finally, the elective modules provide the students a chance for in-depth study of specific aspects of graphics and learning, depending on the interest and intended long-term personal plans. They also give an opportunity to gain better background in more general data analysis, and algorithm design. The modules of this track build upon techniques in efficient design and programming (C++, GPU) as well as tools from applied and pure mathematics (geometry, numerical optimization, probabilistic analysis) while opening the door to developing creative, personal solutions.

This track is also accessible through the Interaction, Graphics & Design IP Paris M1.

More detail on the Image, Vision and Machine Learning track available here.

Future

This track can typically lead to the following programs in Master 2 level:

• École polytechnique and Paris-Saclay University: Master of Science and Technology Artificial Intelligence & Advanced Visual Computing
• Master Mathématiques/Vision/Apprentissage
• Algorithmique et Fondements de la Programmation (/MPRI)
• Traitement Avancé des Images et Vision, Imagerie du vivant
• Intelligence Artificielle et Décision
• As well as many master/graduate programs abroad at universities such as: EPFL, ETH Zurich, Stanford, Carnegie Mellon, Berkeley, McGill, University of British Columbia, etc.

The research internships at the end of the year (3A) are open to a large number of possibilities in computer vision, computer graphics and artificial intelligence. These general areas are studied in both academic and industrial settings. Examples of companies and laboratories, that have hosted students in the past several years include: Ubisoft, Dassault Systèmes, Facebook, Adobe, Apple, Google, Bentley Systems, Parrot, Disney, Lucas Films, EADS, Thales, Siemens, CEA, Inria, etc.

Rules

Students of this track can choose their 4 courses per period from the provided list with the following additional constraint: At the end of the year, you must have attended, at least, to

• 2 images courses selected among CSC_51073_EP, CSC_51074_EP, CSC_52084_EP, CSC_52085_EP (following all of them is possible and even recommended),
• 1 machine learning course selected among CSC_51054_EP, APM_51053_EP, CSC_52082_EP, CSC_52081_EP.

Courses

First period:

• Mandatory courses:

• Non-mandatory courses:

  • CSC_50P15_EP (was INF515)
    Projet de 3A Image-Vision-Apprentissage
  • CSC_51050_EP (was INF550), Wednesday afternoon
    Algorithmique avancée
  • CSC_51052_EP (was INF552), Friday morning
    Data Visualization
  • CSC_51054_EP (was INF554), Monday afternoon / Wednesday afternoon
    Machine and Deep learning
  • CSC_51055_EP (was INF555), Wednesday afternoon
    Constraint-based modeling and algorithms for decision making
  • CSC_51056_EP (was INF556), Friday morning
    Topological Data Analysis
  • CSC_51073_EP (was INF573), Wednesday morning
    Image Analysis and Computer Vision
  • CSC_51074_EP (was INF574), Tuesday afternoon
    Digital Representations and Analysis of Shapes
  • CSC_51085_EP (was INF585), Wednesday afternoon
    Computer Animation
  • APM_51053_EP (was MAP553), Monday
    Foundation of Machine Learning
  • APM_51055_EP (was MAP555), Friday
    Signal Processing

Second period:

• Mandatory courses:

• Non-mandatory courses:

  • CSC_52002_EP (was CSC_EP_5a040), Monday afternoon
    Computer Vision: from Fundamentals to Applications
  • CSC_52062_EP (was INF562), Monday morning
    Géométrie algorithmique : de la théorie aux applications
  • CSC_52080_EP (was INF580), Friday afternoon
    Large scale mathematical optimization
  • CSC_52081_EP (was INF581), Wednesday afternoon
    Advanced Machine Learning and Autonomous Agents
  • CSC_52087_EP (was INF581A), Tuesday afternoon
    Advanced Deep Learning
  • CSC_52082_EP (was INF582), Friday morning
    Introduction to Text Mining and NLP
  • CSC_52084_EP (was INF584), Wednesday morning
    Synthèse d’Image
  • CSC_52085_EP (was INF584A), Friday afternoon
    Real-time AI in Video Games: decisive & collaborative actions
  • APM_52066_EP (was MAP566), Friday
    Statistics in Action
  • APM_52183_EP (was MAP583), Tuesday afternoon
    Apprentissage profond de la théorie à la pratique

Project: CSC_50P15_EP

Internship: CSC_52994_EP

Timetable

First period:

Second period:

Contacts

The track coordinator is Damien Rohmer.

Mathematics and Computer Science (MAT-INFO)

The mathematics and computer science track aims at providing motivated students with a training in both disciplines that allows them to enroll in a second year of master either in mathematics or in computer science (or in one of the rare bi-disciplinary Masters). Avoiding an artificial combination of themes, it emphasises the interfaces between the disciplines and covers themes that complement each other: number theory, algebra, cryptology, logic, foundations of mathematics and computer science, etc.

Future

The advantage of this track is to keep doors open for the second year.

Around Paris, a natural continuation on the computer science side is the IP Paris Master Algorithms and Foundations of Computer Science (which corresponds to the MPRI master); on the mathematics side, masters are available at Université Paris-Sud and Université Pierre et Marie Curie; an excellent bi-disciplinary master exists at Université Denis Diderot, namely Logique Mathématique et Fondements de l’Informatique (LMFI).

Abroad, some of our alumni went to (or were accepted by) Stanford, MIT, etc, and a rather unique program continues the bi-disciplinary training: Oxford’s Mathematics and the Foundations of Computer Science (MFoCS).

Natural careers for which the track is particularly well-suited include, but are not limited to, a research-based / academic career or a career in national security institutions (ANSSI, etc).

Rules

We ask students to reach a satisfying equilibrium between mathematics and computer science, and therefore pick at least 3 courses in mathematics over the two coursework periods.

Courses

First period:

• Mandatory courses:

• Non-mandatory courses:

  • CSC_50P19_EP (was INF519)
    Projet de 3A MAT-INFO
  • CSC_51050_EP (was INF550), Wednesday afternoon
    Algorithmique avancée
  • CSC_51051_EP (was INF551), Tuesday afternoon
    Computational Logic: from Artificial Intelligence to Zero Bugs
  • CSC_51055_EP (was INF555), Wednesday afternoon
    Constraint-based modeling and algorithms for decision making
  • CSC_51056_EP (was INF556), Friday morning
    Topological Data Analysis
  • CSC_51058_EP (was INF558), Friday morning
    Introduction to Cryptology
  • CSC_51063_EP (was INF563), Monday morning
    Introduction to Information Theory
  • FMA_51052_EP (was MAT552), Wednesday morning
    Théorie algébrique des nombres
  • FMA_51056_EP (was MAT556), Friday afternoon
    Groupes, anneaux, modules et représentations
  • FMA_51057_EP (was MAT557), Wednesday afternoon
    Topologie algébrique
  • MDC_51005_EP, Friday afternoon
    Introduction to quantum information and computing

Second period:

• Mandatory courses:

• Non-mandatory courses:

  • CSC_52001_EP (was CSC_EP_5a038), Wednesday afternoon
    Advanced topics in quantum computation and information
  • CSC_52003_EP (was CSC_EP_5a039), Tuesday afternoon
    Advanced quantum circuitry
  • CSC_52061_EP (was INF561), Tuesday afternoon
    Randomization in Computer Science: Games, Networks, Epidemic and Evolutionary Algorithms
  • CSC_52068_EP (was INF568), Friday morning
    Advanced Cryptology
  • FMA_52062_EP (was MAT562), Monday afternoon
    Introduction à la géométrie algébrique et courbes elliptiques
  • FMA_52063_EP (was MAT563), Wednesday afternoon
    Groupes compacts et groupes de Lie

Project: CSC_50P19_EP

Internship: CSC_52991_EP

Timetable

First period:

Second period:

Contacts

The track coordinator is Olivier Bournez.

Optimization

Optimization is a scientific area at the intersection of computer science, applied mathematics, and engineering. It is part of a larger field called decision sciences, and has been described as “the science of better”.

The main motivation today for the study of optimization is the improvement of industrial processes. But there are few areas of knowledge where its application does not extend to. It is intensively used in logistics, transportation, scheduling, resource management, finance; but also biology, chemistry, physics, mathematics, economics, linguistics, philosophy, and more. Within engineering, electrical (CSC_52069_EP), mechanical (APM_52062_EP), and chemical are those on which it has the most important impact.

Optimization rests on a formal descriptive language called mathematical programming (APM_51057_EP, CSC_52069_EP, CSC_52080_EP), which is used to construct a formal model, called mathematical program (MP), of a given optimization problem. An MP is usually solved by an off-the-shelf piece of software called solver. A solver accepts all of the MP instances of a certain class and, given enough time, outputs the solution. Very large-scale MPs that defy general-purpose solvers need more investigation, work and algorithmic creativity to be solved (CSC_52080_EP, CSC_51050_EP). It is sometimes necessary to resort to hybrid approaches involving randomized heuristics (CSC_52061_EP), artificial intelligence (CSC_52081_EP) and machine learning (CSC_51054_EP, APM_51053_EP).

Projects (CSC_50P16_EP) and internships (CSC_52991_EP) can be discussed with the person in charge of the Optimization PA (Leo Liberti). The proposed topics will be at the forefront of active research of the optimization professors and researchers at LIX. We offer opportunities for both theoretical and applied work.

This track is also accessible through the Operational Research IP Paris M1.

Rules

Students of the track must take one of the two courses CSC_51050_EP or CSC_51055_EP (in the non-mandatory list) during the first period (unfortunately, following both is not possible due to scheduling constraints).

Future

This track naturally leads to the following Master 2 programs:

• MPRO (Master Parisien en Recherche Opérationnelle)
• M2 Optimization, Paris Saclay
• M2RO – Recherche Opérationnelle (Toulouse)
• Un parcours Recherche Opérationnelle, Combinatoire et Optimisation est proposé en deuxième année du Master d’Informatique de l’Université Grenobles-Alpes
• Masters abroad: Columbia University, Université de Montreal, Princeton University, …

Courses

First period:

• Mandatory courses:

  • APM_51057_EP (was MAP557), Friday
    Recherche opérationnelle : aspects mathématiques et applications

• Non-mandatory courses:

  • CSC_50P16_EP (was INF516)
    Projet de 3A Optimisation
  • CSC_51050_EP (was INF550), Wednesday afternoon
    Algorithmique avancée
  • CSC_51054_EP (was INF554), Monday afternoon / Wednesday afternoon
    Machine and Deep learning
  • CSC_51055_EP (was INF555), Wednesday afternoon
    Constraint-based modeling and algorithms for decision making
  • APM_51050_EP (was MAP550), Monday afternoon
    Théorie des jeux
  • APM_51053_EP (was MAP553), Monday
    Foundation of Machine Learning
  • APM_51176_EP (was MAP576), Tuesday afternoon
    Emerging topics in Machine Learning - Statistical Theory and Tree-based methods
  • MDC_51005_EP, Friday afternoon
    Introduction to quantum information and computing

Second period:

• Mandatory courses:

  • CSC_52069_EP (was INF569), Monday
    Decision theory, with applications to energy systems
  • CSC_52080_EP (was INF580), Friday afternoon
    Large scale mathematical optimization

• Non-mandatory courses:

  • CSC_52061_EP (was INF561), Tuesday afternoon
    Randomization in Computer Science: Games, Networks, Epidemic and Evolutionary Algorithms
  • CSC_52067_EP (was INF567), Wednesday morning
    Wireless Networks: from Cellular to Connected Objects
  • CSC_52081_EP (was INF581), Wednesday afternoon
    Advanced Machine Learning and Autonomous Agents
  • APM_52062_EP (was MAP562), Wednesday afternoon
    Optimal Design of Structures
  • APM_52064_EP (was MAP564), Wednesday morning
    Réseaux sociaux et de communication: modèles et algorithmes proba
  • APM_52065_EP (was MAP565), Wednesday afternoon
    Modélisation aléatoire et statistique des processus
  • APM_52183_EP (was MAP583), Tuesday afternoon
    Apprentissage profond de la théorie à la pratique

Project: CSC_50P16_EP

Internship: CSC_52991_EP

Timetable

First period:

Second period:

Contacts

The track coordinator is Leo Liberti.

Personalised Track

This tracks is intended to students who want to follow a personalised track in relations with some identified professional project. This track provides access to a wide variety of choices proposed by the computer science department, and allows some pluri-thematics different from the already existing tracks.

The choice of the personalised track should however be the consequence of a reflexion about a professional project in order to ensure some coherence and suitable choice of courses. The integration of students in this track needs some exchanges and some validation from the coordinators of the track. You should at least provide them with the following elements:

• the description of some clearly identified professional project, with an explicit project of year 4, or of a master,
• a course selection, with the explanation of why this is not already covered by some existing track.

Courses

First period:

• Mandatory courses:

• Non-mandatory courses:

  • CSC_50P14_EP (was INF514)
    Projet de 3A du parcours sur mesure
  • CSC_51050_EP (was INF550), Wednesday afternoon
    Algorithmique avancée
  • CSC_51051_EP (was INF551), Tuesday afternoon
    Computational Logic: from Artificial Intelligence to Zero Bugs
  • CSC_51052_EP (was INF552), Friday morning
    Data Visualization
  • CSC_51053_EP (was INF553), Friday afternoon
    Database Management Systems
  • CSC_51054_EP (was INF554), Monday afternoon / Wednesday afternoon
    Machine and Deep learning
  • CSC_51055_EP (was INF555), Wednesday afternoon
    Constraint-based modeling and algorithms for decision making
  • CSC_51056_EP (was INF556), Friday morning
    Topological Data Analysis
  • CSC_51057_EP (was INF557), Wednesday afternoon
    From the Internet to the IoT: Fundamentals of Modern Computer Networking
  • CSC_51058_EP (was INF558), Friday morning
    Introduction to Cryptology
  • CSC_51059_EP (was INF559), Wednesday morning
    Architecture des ordinateurs et systèmes d’exploitation
  • CSC_51063_EP (was INF563), Monday morning
    Introduction to Information Theory
  • CSC_51071_EP (was INF571), Monday morning
    Distributed Data Structures, with a Spotlight on Blockchains
  • CSC_51073_EP (was INF573), Wednesday morning
    Image Analysis and Computer Vision
  • CSC_51074_EP (was INF574), Tuesday afternoon
    Digital Representations and Analysis of Shapes
  • CSC_51075_EP (was INF575), Tuesday afternoon
    Safe Intelligent Systems
  • CSC_51085_EP (was INF585), Wednesday afternoon
    Computer Animation
  • MDC_51005_EP, Friday afternoon
    Introduction to quantum information and computing

Second period:

• Mandatory courses:

• Non-mandatory courses:

  • CSC_52001_EP (was CSC_EP_5a038), Wednesday afternoon
    Advanced topics in quantum computation and information
  • CSC_52003_EP (was CSC_EP_5a039), Tuesday afternoon
    Advanced quantum circuitry
  • CSC_52002_EP (was CSC_EP_5a040), Monday afternoon
    Computer Vision: from Fundamentals to Applications
  • CSC_52060_EP (was INF560), Wednesday afternoon
    Algorithmique parallèle et distribuée
  • CSC_52061_EP (was INF561), Tuesday afternoon
    Randomization in Computer Science: Games, Networks, Epidemic and Evolutionary Algorithms
  • CSC_52062_EP (was INF562), Monday morning
    Géométrie algorithmique : de la théorie aux applications
  • CSC_52064_EP (was INF564), Monday afternoon
    Compilation
  • CSC_52065_EP (was INF565), Monday morning
    Information Systems Security
  • CSC_52066_EP (was INF566), Wednesday afternoon
    From Fundamentals to Reality: How The Internet Really Works — And How To Make It Better
  • CSC_52067_EP (was INF567), Wednesday morning
    Wireless Networks: from Cellular to Connected Objects
  • CSC_52068_EP (was INF568), Friday morning
    Advanced Cryptology
  • CSC_52069_EP (was INF569), Monday
    Decision theory, with applications to energy systems
  • CSC_52080_EP (was INF580), Friday afternoon
    Large scale mathematical optimization
  • CSC_52081_EP (was INF581), Wednesday afternoon
    Advanced Machine Learning and Autonomous Agents
  • CSC_52082_EP (was INF582), Friday morning
    Introduction to Text Mining and NLP
  • CSC_52083_EP (was INF583), Friday afternoon
    Systems for Big Data
  • CSC_52084_EP (was INF584), Wednesday morning
    Synthèse d’Image
  • CSC_52085_EP (was INF584A), Friday afternoon
    Real-time AI in Video Games: decisive & collaborative actions
  • CSC_52086_EP (was INF586), Tuesday afternoon
    Sécurité des réseaux
  • CSC_52089_EP (was INF589), Monday afternoon
    Computational analysis of high-throughtput sequencing date

Project: CSC_50P14_EP

Internship: CSC_52991_EP

Timetable

First period:

Second period:

Contacts

The track coordinators are Olivier Bournez, François Morain.

Quantum computer science

The advent of quantum computing is a major event in the young history of computer science. We are facing a new computational model offering interesting computational advantages while being realisable on concrete physical machines. Such promises make it necessary for computer scientists to take this new model seriously and rigorously. Indeed, while the construction of the quantum computer is a significant engineering challenge involving vast areas of physics, in fine, it is the full range of computer science knowledge acquired in the last decades that will enable us to come up with new protocols and algorithms, to distinguish real opportunities from technical dead ends, to identify obstacles and work towards the deployment of effective quantum solutions. To this end, the quantum computer science program provides a solid background in quantum computation, both in its various algorithmic and cryptographic applications and in the optimisation and error-correction techniques needed to achieve practical computational gains.

The track aims at training scientists, engineers, and IT managers to play an active role in the development of these new technologies and make relevant strategic decisions in the highly dynamic environment of quantum technologies.

Future

While quantum computing leads naturally to specialised master 2 programs, such as the Quantum Information master at Paris-Sorbonne University, it also prepares for general computer science programs, such as the Parisian Master of Research in Computer Science.

Courses

First period:

• Mandatory courses:

  • MDC_51005_EP, Friday afternoon
    Introduction to quantum information and computing

• Non-mandatory courses:

  • CSC_52P01_EP (was CSC_EP_5a041)
    Projet de 3A de Quantique
  • CSC_51050_EP (was INF550), Wednesday afternoon
    Algorithmique avancée
  • CSC_51051_EP (was INF551), Tuesday afternoon
    Computational Logic: from Artificial Intelligence to Zero Bugs
  • CSC_51054_EP (was INF554), Monday afternoon / Wednesday afternoon
    Machine and Deep learning
  • CSC_51058_EP (was INF558), Friday morning
    Introduction to Cryptology
  • CSC_51059_EP (was INF559), Wednesday morning
    Architecture des ordinateurs et systèmes d’exploitation
  • CSC_51063_EP (was INF563), Monday morning
    Introduction to Information Theory
  • CSC_51071_EP (was INF571), Monday morning
    Distributed Data Structures, with a Spotlight on Blockchains

Second period:

• Mandatory courses:

  • CSC_52001_EP (was CSC_EP_5a038), Wednesday afternoon
    Advanced topics in quantum computation and information
  • CSC_52003_EP (was CSC_EP_5a039), Tuesday afternoon
    Advanced quantum circuitry

• Non-mandatory courses:

  • CSC_52060_EP (was INF560), Wednesday afternoon
    Algorithmique parallèle et distribuée
  • CSC_52064_EP (was INF564), Monday afternoon
    Compilation
  • CSC_52068_EP (was INF568), Friday morning
    Advanced Cryptology
  • APM_52061_EP (was MAP561A), Wednesday
    Mathematical modelling of quantum computers
  • PHY_52180_EP (was PHY580), Tuesday afternoon
    Quantum information : entanglement, control and platforms for quantum technologies

Project: CSC_52P01_EP

Internship: CSC_52991_EP

Timetable

First period:

Second period:

Contacts

The track coordinator is Titouan Carette.

The Internet of Everything For a Digitized Society

In the 21th century, the need for ubiquitous connectivity of everything is unquestionable, for example for:

• directly extracting big data from the environment, for analysis and decision-making purposes,
• remotely piloting and monitoring technical installations for safety, environmental, and social/economical objectives
• observing consumption and optimising operation of critical infrastructures, e.g. the next-generation power distribution grid integrating renewable energy, decentralised production, etc.,
• intelligent living, connected healthcare, smart cities and transport, …

Going from internet-scale connectivity of just 10 billion devices today, to ubiquitous connectivity of 50+ billion devices (most of which will be embedded into larger systems) in 10 years, is at the same time an incredible vector of innovation, and a huge technical challenge.

This track covers all aspects of that challenge: from the fundamentals of communication, through internet-scale systems engineering, and towards the technologies that in the near (and, not so near) future will allow this ubiquitous connectivity to fulfil its potential and handle the security and privacy issues that come with it - and, of course, to the theoretical foundations enabling this digitisation revolution.

Courses

The first term (fall) offers foundational skills in concurrency, cryptology, operating systems and computer architectures, as well as modern communications. This includes understanding the principles behind algorithms, protocols, systems, and architectures that make the Internet, and the Internet of Things (IoT), work today. Bluetooth, ZigBee, Arduino, TLS/SSL, …, but also OSPF, and TCP/IP, are some of the technological exponents of these underlying principles, which will be studied through the courses this term.

The second term (winter) embraces “from fundamentals to reality”. Enjoying proximity with industry, this includes looking at how the principles examined during the first term are used in practice. For example, how does the CDN technology (Content Delivery Networking), used by Netflix, YouTube, …, to deliver massive amounts of real-time data across the Internet, actually work? What makes the Mobile Internet (2G, 3G, …) work? — and, how could it be used for connecting not just “cellphones and tablets” but also for providing ubiquitous connectivity for less powerful “objects”? In other words, what’s behind IoT technologies such as 5G, SigFox, or LoRa?

Partnership

This program is partially supported by the academic chair Internet-of-Everything, in partnership between École polytechnique and Cisco Systems.

Courses

First period:

• Mandatory courses:

  • CSC_51057_EP (was INF557), Wednesday afternoon
    From the Internet to the IoT: Fundamentals of Modern Computer Networking
  • CSC_51058_EP (was INF558), Friday morning
    Introduction to Cryptology
  • CSC_51059_EP (was INF559), Wednesday morning
    Architecture des ordinateurs et systèmes d’exploitation

• Non-mandatory courses:

  • CSC_51053_EP (was INF553), Friday afternoon
    Database Management Systems
  • CSC_51054_EP (was INF554), Monday afternoon / Wednesday afternoon
    Machine and Deep learning
  • CSC_51075_EP (was INF575), Tuesday afternoon
    Safe Intelligent Systems
  • APM_51053_EP (was MAP553), Monday
    Foundation of Machine Learning

Second period:

• Mandatory courses:

  • CSC_52066_EP (was INF566), Wednesday afternoon
    From Fundamentals to Reality: How The Internet Really Works — And How To Make It Better
  • CSC_52067_EP (was INF567), Wednesday morning
    Wireless Networks: from Cellular to Connected Objects
  • CSC_52086_EP (was INF586), Tuesday afternoon
    Sécurité des réseaux

• Non-mandatory courses:

  • CSC_52083_EP (was INF583), Friday afternoon
    Systems for Big Data

Project: no project

Internship: CSC_52991_EP

Timetable

First period:

Second period:

Contacts

The track coordinators are Thomas Clausen, Juan Antonio Cordero Fuertes.

Bioinformatics

This program is joint between the computer science and biology department and is detailed on the dedicated webpage.