Evaluation of M2 courses

For your information, here are the modalities of evaluation for all M2 courses.

CR01: The students are given research articles twice. Each time, they give an oral presentation based on these articles. The first one counts as continuous evaluation (CE), the second one as final evaluation (FE). The final note is given by (CE+2FE)/3.

CR02: There is homework for continuous evaluation (CE). The students are given research articles, they produce a written report and give an oral presentation based on these articles (FE). The final note is given by (CE+2FE)/3.

CR03: There is homework for continuous evaluation (CE). The students are given research articles, they produce a written report and give an oral presentation based on these articles (FE). The final note is given by (CE+2FE)/3.

CR04: There is homework for continuous evaluation (CE). The students choose research articles, they produce a written report and give an oral presentation based on these articles (FE). The final note is given by (CE+FE)/2.

CR05: 3 homeworks for continuous evaluation (CE); written report and give an oral presentation based on a research article for final evaluation (FE). The final note is given by (CE+2FE)/3.

CR06: Two mini-projects counting each for 1/4 of the final grade. One final exam counting for the last half.

CR07: Two homeworks for continuous evaluation (CE1,CE2). The students are given research articles, they produce a written report and give an oral presentation based on these articles (FE). The final note is given by ((CE1+CE2)/2+2FE)/3.

CR08: Grades will be based on a final exam and on the presentation of a research article, each contributing to one half of the grade.

CR09: The students are given research articles, they produce a written report (WR/20) (between 8 and 20 pages). We will do a cross review. One report of another given student report (1 or 2 pages maximum) will be done (RR/20). And student gives an oral presentation based on these articles (OP/5). Questions should be done by students (Q/5). The final note is given by (3WR+3OP+2*(RR+Q))/7

CR10: The students are asked to write a static analyser for a mini language (AS). The students are given research articles, they give an oral presentation based on these articles (FE). The final note is given by (AS+FE)/2.

CR11: There will be a programming project (6pts, due after the mid-term break), a modeling project (7pts, due after the end-of-term break) and a written exam (7pts).

CR12: The final grade for the CR12 course is computed as follows: small exercices to be handed each week, during the first half of the course, count for 1/4 of the grade; a mid term exam counts for 1/4 of the grade; a final exam counts for 1/2 of the grade.

CR13: There is homework for continuous evaluation (CE). The students have also a final evaluation (FE) based on a written exam at the end of the course. The final note is given by max(FE,(CE+2FE)/3).

CR14: There is homework (based on the content of lectures as well as the study of research papers) for continuous evaluation (CE). There is a written exam (2h) for final examination (FE). The final note is given by (CE+2FE)/3.

CR15: The evaluation of the CR15 Complex Networks course will depend on whether the student is involved in the M2 Complex System program or not. If yes then the student’s participation will be mandatory to the TD, which is evaluated through projects during the semester and the final mark will count as 1/3 to the final overall mark. In addition every student has to pass a written exam by the end of the semester for the lecture, which will count with 2/3 weight in the final evaluation. If the student is not involved in the Complex System program only the written exam is mandatory, which result will determine 100% the final mark. On the other hand even these students have the option to participate to the TDs, complete the projects and get a TD mark. In this case the student will have the advantage to gain the better mark gained with or without the TD results.

CR16: The students will be proposed research articles about which they need to produce a report (with some numerical applications of the content of the article) and an oral presentation. The report and presentation are used for the note.

CR17: Two homeworks (counting each for DM/2) and one final exam (DS). The final grade is (DM+2DS)/3.

CR18: The students are given 3 homework assignments for continuous evaluation (CE). At the end of the semester, students are asked to study and orally present research articles (FE). The final note is given by (CE+2FE)/3.

CR19: There is homework for continuous evaluation (CE). The students are given research articles, they produce a written report and have an exam based on these articles (FE). The final note is given by (CE+2FE)/3.

ER01: Randomized Algorithms (7-11 December)

Dates : 7-11 December

Teachers: Joel Ouaknine, Ben Worrell et Stefan Kiefel (Oxford).
Local contact: Pascal Koiran

Title: Probabilistic Techniques and Models in Computer Science

The schedule:
Monday: 9:30 – 11:30 am and 1:30 – 3:30 pm
Tuesday: 9 – 11:30 am, 1:30-3:30 pm and 4-6 pm.
Wednesday: 9-11:30 am and 1:30-3:30 pm.
Thursday: 9 am – 12:30 pm
Friday: 9 am – 12:30 pm and afternoon exam: 2 pm – 4 pm

Synopsis (more information here):

— Decision Problems

* Space-bounded interactive protocols

* Reachability and threshold problems for Markov chains

* Connections with number theory

— Stochastic Processes

* Markov-chain Monte Carlo techniques, Coupling

* Martingales, Optional Stopping Theorem, Azuma’s inequality and
applications, Lyapunov functions

* Equivalence of Markov chains, Markov decision processes

* Distance between Markov chains

* Analysis of infinite-state Markov chains

— Data Structures and Algorithms

* Luby’s algorithm

* Count-min filters

* Random rounding, packet routing

— Learning Theory

* Rademacher complexity, VC dimension

* Johnson-Lindenstraus Lemma

ER02: Data Mining : Statistical Modeling and Learning from Data (11-15 January)

Dates: 11-15 January 2016

Teachers: Ciro Cattuto, Laetitia Gauvin et André Panisson (ISI Torino)

Local contact: Márton Karsai (marton.karsai@ens-lyon.fr)

Venue: ENS Lyon, site Monod, Amphi B (entrance from the 4th floor)

Time: 9:30 – 16:45

External participants who has no access to the building should contact Marton Karsai (marton.karsai@ens-lyon.fr) in advance.

The main page of the course can be found here.

The course aims to provide basic skills for analysis and statistical modeling of data, with special attention to machine learning both supervised and unsupervised. An important objective of the course is the operational knowledge of the techniques and algorithms treated, and for this aim the lectures will focus on both theoretical and practical aspects of machine learning, and for the practical part it is required to have a good knowledge of programming, preferentially in Python language. The expected outcomes include (1) understanding the theoretical foundations of machine learning and (2) ability to use some Python libraries for machine learning in the context of simple applications.

Topics will include:

– The major paradigms of learning from data, the learning problem, the feasibility of learning
– The architecture of machine learning algorithms: model structure, scoring, and model selection ­ The theory of generalization, model complexity, the approximation­generalization tradeoff, bias and variance, the learning curve
– Score functions and optimization techniques. Gradient descent and stochastic gradient descent.
– Validation and Cross­Validation: validation set, leave­one­out cross validation, K­fold cross­validation
– Linear Models: linear classification, linear regression, ordinary least squares, logistic regression, non­linear transformations
– Non­linear models for classification: support vector machines, tree models, nearest­neighbor methods, Naive Bayes
– Overfitting and Regularization: model complexity and overfitting, commonly used regularizers, Lasso.
– Unsupervised learning: cluster analysis, the K­means algorithm, hierarchical clustering
– Feature selection and dimensionality reduction: Singular Value Decomposition, Matrix Factorisation
– Information retrieval, text representation and classification, term weighting

Overview of the theoretical aspects of machine learning will be followed by the application of algorithms in real problems such as: image classification, text mining, spam detection… The exercises will be implemented with the help of an interactive Python environment, with the use of standard tools for data analysis and visualization, such as the Scientific Python stack, Scikit­Learn, Pandas and NLTK.

Evaluation: personal projects with oral presentation

Registering at Lyon1

Here are some useful informations for your registration at Lyon1.
  • What do you need to pay?
    • Sécurité sociale : 215 € (you are not all concerned)
    • Médecine préventive universitaire : 5,10 €
    • Master : 256 €
  •  Those of you who were at ENS Lyon last year can do everything online after Saturday Sept. 12 (we first need to confirm to Lyon1 who is indeed accepted to the M2): http://inscriptionweb.univ-lyon1.fr/
  • For those of you who enter ENS Lyon this year, you will need to go there. First, you need to fill this document:
Also, you can find the list of documents required here: docs-Lyon1.
You can get some help to fill the registration documents thursday between 11h30 and 13h00 (see the “circulaire de rentrée”), and then some of you (5 at most) can directly go to register at Lyon1 at 13h. You will need a way to pay, and someone with basic knowledge of french because no-one speaks english there. Please let me know asap if you want to go thursday, and I will then send you directions.
We will arrange another time to register for those who could not make it on thursday afternoon.

Timetables for the M2 2015-2016

The pre-course meeting for the M2 is planned Friday September 11 at 9am in Amphi B.

Courses start September 14. Here is the typical timetable for all weeks: EdT-M2-type

Note that the schedule will slightly change from one week to another. Timetables will be posted (and sent by email to students registered to the M2) whenever they are available.

Here is the form to fill for your choices of courses: modules-M2IF

  • Week 38 (starting Sept 14): EdT-M2-S38 (new version of Sept 11)
  • Week 39 (starting Sept 21): EdT-M2-S39
  • Week 40 (starting Sept 28): EdT-M2-S40-v3 (a few changes of rooms / updates of courses)
  • Week 41 (starting Oct 5): EdT-M2-S41-v2 (exchange CR01-CR02)
  • Week 42 (starting Oct 12): EdT-M2-S42
  • Week 43 (starting Oct 19): EdT-M2-S43-v2 (update of room)
  • Holidays
  • Week 45 (starting Nov 2): EdT-M2-S45
  • Week 46 (starting Nov 9): EdT-M2-S46-v2
  • Week 47  (starting Nov 16): EDT-M2-S47
  • Week 48 (starting Nov 23): EDT-M2-S48
  • Week 49 (starting Nov 30): EDT-M2-S49
  • Week 50 (starting Dec 7): Research school, no courses
  • Week 51 (starting Dec 14): EDT-M2-S51
  • Exams will be held on January 4-8, 2016 (EDT-M2-EXAMS), followed by two weeks of research schools.

M1 2015-2016, useful information

This page gathers useful informations for students following the first year of Master in Computer Science at École Normale Supérieure de Lyon. See here for a description of the M1 year. Here for the rules of the game.

Back to school.

Courses will start on september the 14th. See here for a schedule of that week. A (mandatory) meeting is organised on september 10th, at 15.30, in Amphi I (slides of the presentation, slides about english & other languages). The organisation of the year, and several other relevant topics, will be discussed. The venue will be announced later. There is no dress code. A meeting of the whole Département d’Informatique will take place on monday, sept. the 14th, at 16.00, in atrium Mérieux (not far from the fountain on the round square next to the Monod site of ENS).

First semester.

Here is the typical week for the first semester, which will serve as a reference starting on sept. the 14th. Be aware that along the semester, local changes to the schedule may apply: refer to the emails you receive (typically, on thursday or friday for the following week). Here is the schedule for the first week, 14-18 sept. Here is the “fiche de choix de modules”, to be printed, signed with your tutor, and given to D. Hirschkoff.

Midterm exams (beware, this is list is not necessarily exhaustive, and is only there to help you — please refer to the actual course to learn about exams/homeworks/etc.): Information Theory as well as Parallel and Distributed Algorithms and Systems, nov. 9th, Compilers and Program analysis on nov.17th, Performance Evaluation and Networks on nov. 13th nov. 18th, Optimisation&Approximation nov. 20th.

The exams for the first semester will take place in the week jan.25-29. Here is the schedule.

Research schools.

M1 students should validate at least two research schools. See this webpage for a list of the research schools proposed in this academical year. Here is the fiche de choix d’écoles de recherche.

Second semester.

The second semester will last between february 1st and april 26th. The exams will take place between april 27th and may 4th.

The schedule for the first weeks is available here. The plan is to organise, after a couple of weeks, some overlap between courses, in order to have thursday afternoon free, and to gain more flexibility in the handling of the schedule.

Here is the fiche de choix de modules, to be given to D. Hirschkoff for february 16th, at noon.

Partiels (be aware that this list may be non-exhaustive, it contains the information I am aware of): computer algebra march 14th / computational complexity march 22nd / cryptography and security march 18th / april 6th: CGDI project due.

Exams : here is the schedule.

Internship.

Here are the slides of the meeting about internship that took place on oct. the 6th, 2015 (nota: we are not sure yet about the date for the defense: it could be end of august or beginning of september).

Important dates:

  • march 15th: deadline to apply to ENS fundings for travel (see the slides)
  • march 31st: Internship contract data entered in Elipse
  • april 15th: internship contract sent abroad
  •  may 16th 9th: leaving for the internship

The procedure for the preparation of the internship contract (convention de stage) is described here.

Deadlines and advices for the evaluation of the internship: see here.

Modes of interaction, all along the year.

For administrative matters, please contact Amel Zagrarni (secrétariat du Département d’Informatique). To discuss scientific/academical matters, as well as your training period, your future, etc., you can contact your tutor (the list of tutors is here), or Daniel Hirschkoff. Your délégués (representants of the students) are Victor Hublitz, Raphaël Monat and Étienne Moutot. Remember that you are supposed to read your email @ens-lyon.fr — in case of a technical problem, get in touch as soon as possible with the Direction des Systèmes Informatiques).

Next year

  • Préparation à l’agrégation { this information is not relevant if you don’t speak french } : il y a 12 ECTS à valider par le biais de modules de professionalisation.Cette page, qui sera régulièrement mise à jour, décrit l’ensemble des cours qui sont proposés (ainsi que les modalités d’inscription). À noter que cela peut être une bonne idée de valider ces modules l’année qui précède la préparation à l’agrégation, mais on ne peut pas les valider plus d’un an en avance (si vous souhaitez préparer l’agrégation après le M2, c’est en M2 que vous pourrez suivre et valider ces modules).
    Les gens qui ont manifesté un intérêt potentiel/hypothétique pour la préparation à l’agrégation l’an prochain sont: Carette Titouan, Combette Guillaume, Faron Maxime, Iannetta Paul, Lajou Dimitri, Lebeau Fabrice, Lucas Christophe, Mauras Simon, Ohlmann Pierre, Perrotin Elise, Seif Johanna
  • M2: you might be interested in applying for the M2 at ENS Lyon, but maybe also somewhere else, depending on your scientific interests. It is up to you to find out about the corresponding application procedure, and about the dates. Usually applications can be made around may-june. (You may well apply to several Masters, be accepted in several places, take your decision during the summer, and inform everybody of your choice — including places where you don’t go)
    Important notice: alas, it might be the case that you will not validate your M1, or that you validate it but will not be accepted to our local M2. It is up to you to judge whether you run the risk of ending up in this situation or not. It is also up to you to be prepared for all possibilities, and, if applicable, to apply to a different Master, at M1 level. Do it in advance, september is usually too late.

Image processing, digital and computational geometry

Image processing, digital and computational geometry

Course offered in the second semester of M1.

Objectives of the course:

The objective of this course is to introduce fundamental notions of image processing, digital geometry and computational geometry.  The first lectures will be dedicated to image processing (filtering, smoothing, morphological mathematics, ..) and shape representation. Then, we will focus on theoretical and algorithmic issues involved in the analysis of digital shapes. During this analysis of  digital geometry processing tools, we will have to present and integrate some tools from various fields: discrete mathematics, combinatorics, arithmetics, computational geometry, ..

Course contents:

  • Image/Shape representation
  • Image processing (filtering, segmentation)
  • Digital Geometry for shape analysis
  • Computational geometry and data structures
  • Requirements: basic notions of algorithmics

References:

  • Géométrie discrète et images numériques, D. Coeurjolly, A. Montanvert and J.-M. Chassery, Ouvrage collectif, Traité IC2, Hermès, 416 pages, 2007
  • Digital Geometry: Geometric Methods for Digital Picture Analysis, Reihnard Klette, Azriel Rosenfeld, Morgan Kaufmann, 2004
  • Computational Geometry: Algorithms and Applications, Mark de Berg, TU Eindhoven (the Netherlands) Otfried Cheong, KAIST (Korea),Marc van Kreveld, Mark Overmars, Utrecht University (the Netherlands), Springer-Verlag

Computational Complexity

Computational Complexity

Course offered in the second semester of M1.

Overview of the course:

Computational complexity aims to classify computational problems depending on the resources they need. One
studies various modes of computation such as deterministic, randomized, nondeterministic or quantum and compares
resources such as time or space needed to solve algorithmic problems. The objective of this course is to give a
broad understanding of the notions used to classify computational problems. About half of the course is dedicated
to studying basic complexity classes defined using Turing machines. We introduce (or study deeper) notions that are
central in complexity theory: nondeterministic computation (e.g., the class NP), reductions between computational
problems (e.g., NP-completeness) and the technique of diagonalization (e.g., hierarchy theorems). We also study
randomized computation and computation using boolean circuits as well as their relation to basic complexity classes.
We conclude the course by studying the complexity of communication, i.e., trying to evaluate communication
bottlenecks to perform a given computational task between different parties.
Teaching in 2014: Omar Fawzi (lectures) and S ́ebastien Maulat (exercise classes)

Course objectives:

One can summarize the most important objectives of the course as follows.

  1. Understand the formal definitions for the basic complexity classes like L, NL, P, NP, coNP, PSPACE.
    Be familiar with nondeterministic computation and the polynomial hierarchy. Know about the inclusions and separations between these classes.
  2. Understand the notion of reduction between computational problems, and the notion of complete problem, e.g., SAT is NP-complete, PATH is NL-complete, TQBF is PSPACE-complete.
  3. Understand complexity classes defined using boolean circuits, and the notion of uniformity in computation. Know the relation to basic complexity classes.
  4. Understand complexity classes using randomized computations. Know the relation to basic complexity classes.
  5. Get a flavour for the power of interactive proofs.
  6. Be familiar with an important tool in theoretical computer science: communication complexity. Be able to reduce various problems to a communication complexity problem.

ER01: Data Structures for Big Data

Dates: January 12-16, 2015.

Teachers: Michael Bender (Stony Brook University), Martín Farach-Colton (Rutgers and Tokutek), Samuel McCauley (Stony Brook University)

Description of the school

Course Hours

Monday-Thursday:

  • morning lecture: 9:30am-11:30am

  • afternoon lecture: 1:30pm-3:30pm.

  • recitation/homework practice: 4pm-5pm.

Friday:

  • morning lecture: 9:30am-11:30am

  • afternoon exam:  2pm-5pm

Local contact :  Frédéric Vivien.

Registration

Registration is free, but the number of participants is limited. Registration includes neither housing nor meals (though for lunch the attendees will be granted access to the student cafeteria). Registration should be made before … (to be decided later) by clicking on this link, filling the form and sending the e-mail message. You will receive a confirmation as soon as possible.

Alternatively, you can copy/paste and fill the form below, then send it by e-mail to nicole.meftah@ens-lyon.fr with the subject line “Registration form — research school 1”


First Name:
Last Name:
Institution:
Position (MSc student, PhD student, researcher, etc.):
E-mail address: 

wishes to attend the research school “ER01: Algorithmic Game Theory”, taking place at ENS Lyon, from Jan. 12 to Jan. 16, 2015.

ER02: Algorithms and Heuristics for Large-scale Data Sets

Dates: January 19-23, 2015.

Dedicated webpage

Teachers: Nicolas Schabanel (Université Paris-Diderot), Alain Barrat et Bruno Gonçalves (Université Aix-Marseille)

Local contact : Marton Karsai

Registration

Registration is free, but the number of participants is limited. Registration includes neither housing nor meals (though for lunch the attendees will be granted access to the student cafeteria). Registration should be made before … (to be decided later) by clicking on this link, filling the form and sending the e-mail message. You will receive a confirmation as soon as possible.

Alternatively, you can copy/paste and fill the form below, then send it by e-mail to nicole.meftah@ens-lyon.fr with the subject line “Registration form — research school 2”


First Name:
Last Name:
Institution:
Position (MSc student, PhD student, researcher, etc.):
E-mail address: 

wishes to attend the research school “ER02: “, taking place at ENS Lyon, from Jan. 19 to Jan. 23, 2015.

ER03: Static Analysis and Compilation

Dates: January 26-30, 2015.

Dedicated webpage

Teacher: Fernando Magno Pereira (Univ Mineas Gerais, Brazil)

Local contact :  Laure Gonnord

Registration

Registration is free, but the number of participants is limited. Registration includes neither housing nor meals (though for lunch the attendees will be granted access to the student cafeteria). Registration should be made before … (to be decided later) by clicking on this link, filling the form and sending the e-mail message. You will receive a confirmation as soon as possible.

Alternatively, you can copy/paste and fill the form below, then send it by e-mail to nicole.meftah@ens-lyon.fr with the subject line “Registration form — research school 3”


First Name:
Last Name:
Institution:
Position (MSc student, PhD student, researcher, etc.):
E-mail address: 

wishes to attend the research school “ER03: Static Analysis and Compilation”, taking place at ENS Lyon, from Jan. 26 to Jan. 30, 2015.

Assistant professor position

 

Job opening — Computer Science Department / LIP laboratory, ENS de Lyon

Open assistant professor position (maître de conférences) in Computer Science

The LIP research laboratory and the Computer Science teaching department are recruiting a tenured assistant professor in Computer Science.
The job description is available here.

Ranking of the jury:
(the ranking will become final only after the decision of the administration council of ENS de Lyon)
1- Dagand Pierre-Evariste
2- Fawzi Omar
3- Huguenin Kevin
4- Tzameret Iddo
5- Cohen Cyril

Composition of the selection committee: Anne Benoit, Pascal Bouvry (président), Hubert Comon, Arnaud Durand, Guillaume Hanrot, Sylvain Joubaud, Florence Maraninchi, Antoine Miné, Lucas Nussbaum, Natacha Portier, Damien Stehlé, Laurent Théry.

Teaching contact: damien.stehle@ens-lyon.fr and stephan.thomasse@ens-lyon.fr
Research contact: guillaume.hanrot@ens-lyon.fr and gilles.villard@ens-lyon.fr