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2017-18 News

!!Application for the MSIAM M1 program 2017-2018 are now closed.

Application for the MSIAM M1 program 2018-2019 will open in January.

!! Start of term will be on September 4th at 9am !! It will take place at Room F112, IM2AG, 60 rue de la Chimie, Saint Martin d'Hères

2017-18 Information

Download here the academic planning for 2017-2018 M1 MSIAM (1st year).

Download here the first term schedule for 2017-2018 M1 MSIAM (1st year).

The first week is organized as follows:

  • Start-of-term information and registration meeting (your attendance is compulsory!): 9am Sept. 4th, room F112 at UFR IM2AG, which is located 60 Rue de la Chimie, in Saint-Martin-d'Hères.
  • Roughly a week of remedial and compulsory background mathematics and computing sciences classes, during which administrative interviews will be planned with the staff.
  • First term will start immediately after.

MSIAM First Year - Courses list

The first semester consists in compulsory background courses. Second semester will include, if the headcount makes it possible, elective courses depending on your interests. You will find below short descriptions of the courses contents.

First semester compulsory courses:

First semester language course:

Second semester compulsory courses:

Second semester elective courses (choose 2):



Algorithms and software tools

3 ECTS, CTD 12h, TP 24h

Course objective: The objective of this course is to present the computer sciences basics useful for applied mathematics.

Course contents:

  • Compilation (const, inline, loops, Gnu Make …)
  • C++: genericity (template), code reuse (STL), efficient programming
  • Objects and hierarcical memory, notions of cache and locality (e.g., BLAS)
  • Basics of algorithmics
  • Complexity
  • Error propagation, floating point computing

This course relies on practical sessions.

Grading:

  • 1/2 practical
  • 1/2 final written exam

(head: Laurence Pierre)


Applied probability and Statistics

6 ECTS, CM 24h, TP 24h

Mutualized with M1 SSD Applied probability and Statistics

The aim of this course is to provide basic knowledge of applied probability and an introduction to mathematical statistics.

Contents:

  • Applied probability
  • Estimation (parameter)
  • Sample comparison
  • Statistical tests

This course includes practical sessions.

Grading:

  • 1/2 for the applied proba part
  • 1/2 for the statistics part

See the contents from the associated course at UPMF.


Partial differential equations and numerical methods

6 ECTS, CM 16.5h, TD 16.5h, TP 16.5h

Course objectives:

Give an overview of modelling using partial differential equations.

Course contents:

  • Types of equations, conservation laws
  • Finite differences methods
  • Laplace equation
  • Parabolic equations (diffusion)
  • Hyperbolic equations (propagation)
  • Non linear hyperbolic equations

This course include practical sessions.

This is a two parts course:

  1. Course mutualized with Ensimag 2A 4MMMEDPS (head: Eric Blayo)
  2. MSIAM specific course (in-depth and practical session) (head: Maelle Nodet)

Grading:

  • 1/2 practical
  • 1/2 final written exam

Signal and image processing

6 ECTS, CTD 36h, TP 18h

The aim of this course is to provide the basics mathematical tools and methods of image processing and applications.

Contents:

  • Image definition
  • Fourier transform, FFT, applications
  • Image digitalisation, sampling
  • Image processing: convolution, filtering. Applications
  • Image decomposition, multiresolution. Application to compression

This course includes practical sessions.

Grading:

  • 1/2 practical
  • 1/2 final written exam

(head: Cécile Amblard)


Geometric modelling

6 ECTS, CM 16.5h, TD 19.5h, TP 18h

The aim of this course is to present spline curves and surfaces: they are routinely used in geometrical design softwares, such as CATIA or MAYA. They have many applications: 3D design of prototypes, medical imaging, image synthesis, geographical imaging.

Course Contents:

  • Affine and differential geometry basics
  • Curves: Bezier, Bernstein polynoms, B-spline
  • Surfaces: Bezier, spline interpolation and spline approximation
  • Applications

This course include practical sessions.

Grading:

  • 1/2 practical
  • 1/2 final written exam

(head: Boris Thibert)


Français langue étrangère

3 ECTS

French as a second language.

Organised by CUEF: cuef.xtek.fr

Important dates:

  • Application deadline: Sept 16th
  • Compulsory test: Sept 27th 6:00pm

Annual calendar for FLE


English

3 ECTS

English for french-speaking students.

(head: Virginia Gardner)



Toward big data and high performance computing

6 ECTS, CTD 18h, TP 36h

The aim of this course is to give an introduction to numerical and computing problematics of large dimension problems.

Contents:

  • Introduction to database
  • Introduction to big data
  • Introduction to high performance computing (HPC)
  • Numerical solvers for HPC

This course relies on practical sessions.

(head: Christophe Picard)


Modeling activity 1: Project

3 ECTS

January science and/or industrial project.


Modeling activity 2: Internship

3 ECTS

Industrial and/or research internship.


Numerical Optimization: mathematical background and case studies

6 ECTS, CTD 36h, TP 18h

This program combines case studies coming from real life problems or models and lectures providing the mathematical and numerical backgrounds.

Course contents:

  • Introduction, classification, examples.
  • Theoretical results: convexity and compacity, optimality conditions, KT theorem
  • Algorithmic for unconstrained optimisation (descent, line search, (quasi) Newton)
  • Algorithms for non differentiable problems
  • Algorithms for constrained optimisation: penalisation, SQP methods
  • Applications and case studies

This course includes practical sessions.

Lab:

Main results:

(head: Laurent Desbat)


Variational methods applied to modelling

Course in 2 parts:

  • 3ECTS = CM 16.5h + TD 16.5h
  • 3ECTS = CTD 3h + TP 18h

Course objectives:

The aim of this course is to get deep knowledge of PDE modelling and their numerical resolution, in particular using variational methods such as the Finite Elements method.

Course contents:

  • Introduction to modelling with examples.
  • Boundary problem in 1D, variational formulation, Sobolev spaces.
  • Stationary problem, elliptic equations.
  • Finite element method: algorithm, errors…
  • Evolution models, parabolic equations, splitting methods
  • Extensions and applications, FreeFEM++

This course include practical sessions.

This is a two parts course:

  1. Course mutualized with Ensimag 2A 4MMMVAM (head: Emmanuel Maitre)
  2. MSIAM specific course (in-depth and practical session) (head: Clément Jourdana)

3D Graphics

Course in 2 parts:

  • 3ECTS = CTD 16.5h + TP 16.5h
  • 3ECTS = CTD 19.5h + TP 1.5h

Course objectives:

The aim of this course is to give mathematical grounds and algorithms for the modelling, animation, and synthesis of images.

Contents:

  • Projective rendering methods
  • Animation, cinematic methods
  • Geometrical modelling, 3D, deformation
  • Case study

This course include practical sessions. Implementation using OpenGL.

This is a two parts course:

  1. Course mutualized with M1 MoSIG
  2. MSIAM specific course (in-depth and practical session)

Computer Algebra and Cryptology

Course in 2 parts:

  • 3ECTS = TD 19h + TP 15h
  • 3ECTS = TD + TP

Course objectives: The aim of this course is to give mathematical grounds of security, integrity, authentication and cryptology.

Course contents:

  • Binary encoding of information
  • Zn* group, field theory
  • Symetric cryptography
  • Asymetric cryptography, RSA
  • Hash, DSA
  • Lossless compression
  • Error correcting codes
  • Linear codes
  • Cyclic codes

This course include practical sessions.

This is a two parts course:

  1. Course mutualized with Pure Mathematics M1 (head: François Dahmani)
  2. MSIAM specific course (in-depth and practical session) (head: Jean-Guillaume Dumas)

Data analysis, linear models and ANOVA

Course in 2 parts:

  • 3ECTS = CM 13h + TD 5h + TP 15h
  • 3ECTS = CTD 14h + TP 6h

Course objectives:

The aim of this course is to present advanced statistics and linear modelling, variance analysis and provide practical implementation

Contents:

  • Principal components analysis (PCA)
  • Clissification (Linear Discr. Analysis)
  • Data mining (text mining)
  • Linear regression
  • Estimation and test of regression parameters
  • ANOVA
  • ANCOVA
  • Practical implementation

This course include practical sessions.

This is a two parts course:

  1. Course mutualized with Ensimag 2A 4MMFDASM (head: Jean-Baptiste Durand)
  2. MSIAM specific course (in-depth and practical session) (head: Clémentine Prieur)

ECTS total

30 ECTS per semester:

  • 1st semester compulsory courses 27 ECTS
  • 1st semester language course 3 ECTS
  • 2nd semester compulsory courses 18 ECTS
  • 2nd semester with choice 12 ECTS
m1courses.txt · Last modified: 2017/09/12 11:32 by picard
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