The first semester of MSIAM master 2 is essentially divided in two tracks. Each student should be registered in one of the following tracks:

However a personalized track may also be build for some students from the available courses (if no timetable conflicts appears). The personalized tracks must be approved by the Professors in charge of MSIAM.

- 30 ECTS scientific courses (3 or 6 each, excluding French language course).
- 6 ECTS may be chosen by the students outside of the MSIAM offer (needs no timetable conflict and approval by the MSIAM heads).

Visit for example the current fundamental mathematics offer.

Modelling, Scientific Computing, Imaging, Geometry, CAD have been for decades at center of many innovations in many areas: design and development in industry such as transports, manufacturing (any innovative object is concerned by MSCI and CAD), Medical / Pharmaceutical (modelling of systems [CT scanner, MRI, hybrid imaging, robots, etc.], life and biomedical modelling), Chemical (modelling and simulation of reactions), Environment, Big Data (data and image modelling and analysis) …

The purpose of the MSCI track is to train both high-level researchers and engineers in Modelling, Scientific Computing, Imaging, by providing theoretical foundations and applied methodology. The theoretical courses ( 144h to 180h ) may be completed by more in-depth study of some courses and associated projects or projects from the Industry (see Modelling Seminar and Projects). They are followed by an internship in a research lab or company. This track is preparing students both for research in applied maths and also for high level applications of mathematics, modelling and computing in wide areas in the industry.

The burst of data collection at unprecedented speed and scale in many fields, from biology to astrophysics, demands a paradigm shift in applied mathematics and computer science in order to face the new challenges in scientific modelling and computation.

To harness the power of this data revolution, the world needs academic researchers and professionals called “data scientists” skilled in designing and utilizing automated methods of analyzing it. The Data Science track in the MSIAM master aims at establishing the country’s leading Data Science academic training. Data science is becoming essential to answer some of the big scientific questions and technological challenges of our times: How can we prevent cancer and find better cures for diseases? How does the brain work? How can we design an artificial intelligence?

Data science lies at the crossroad of mathematics (pure and applied), statistics, computer science and an increasingly large number of application domains.

The University of Grenoble Alpes benefits from a very active community in data science, whose most visible banner is the Grenoble Data Science Institute. Among its permanent groups and recurrent activities are the Grenoble Data Club and R-in-Grenoble seminars.

The Data Science track has common courses with the MoSIG program. The Data Science track is both research- and industry-oriented. Its purpose is to train high-level researchers with skills in both the mathematical aspects of Data Science and in practical skills in data analysis and programming.

The theoretical courses (~180h) are followed by an internship in a research lab or company.

Some courses in DS focus on the methods and mathematical results on which rely the main approaches in machine learning, optimization and data science. They are oriented towards acquiring knowledge in machine learning, probabilistic and statistical modelling and optimization.

Some others focus on large-scale (often meaning high-dimensional) aspects of data science. They are dedicated to large-scale databases, optimization and machine learning. Some of them focus on some given applications, such as biology, information retrieval in multimedia databases or object recognition in images (typically, using deep learning approaches).

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#### Advanced Algorithms for Machine Learning and Data Mining

#### Advanced Imaging

#### An introduction to shape and topology optimization

#### Category learning and object recognition aka Machine learning for computer vision and audio processing

#### Computational biology

#### Data science seminar

#### Efficient methods in optimization

#### Fundamentals of probabilistic data mining

This courses introduces probabilistic models with latent variables, and the associated algorithms to estimate the parameters and perform inference over the latent variables.

#### Geophysical imaging

#### GPU Computing

#### Information access and retrieval

This course addresses advanced aspects of information access and retrieval, focusing on several points: …

#### Introduction to extreme-value analysis

#### Kernel methods for machine learning

#### Level set methods and optimization algorithms with applications in imaging

#### Machine learning fundamentals

#### Model exploration for approximation of complex, high-dimensional problems

#### Model selection for large-scale learning

#### Modeling Seminar

#### Numerical optimal transport and geometry

#### Software Development Tools and Methods

#### Statistical methods for forecasting

#### Stochastic Calculus and Applications to Finance

#### Wavelets and applications

A prior algorithms (Frequent item sets) & Page Rank Monte-carlo, MCMC methods: Metropolis-Hastings and Gibbs Sampling…

In this course, we will first focus on linear methods for image denoising.

In a very broad acceptation, shape and topology optimization is about finding the best domain (which may represent, depending on applications, a mechanical structure, a fluid channel,…) with respect to a given performance criterion (e.g. robustness, weight, etc.), under some constraints (e.g. of a geometric nature).

This course addresses advanced aspects of information access and retrieval, focusing on several points: …

This interdisciplinary MSc course is designed for applicants with a biomedical, computational or mathematical background.

This courses introduces probabilistic models with latent variables, and the associated algorithms to estimate the parameters and perform inference over the latent variables.

Theoretical foundations of convex optimization.

Understanding Earth’s interior mechanisms, assessing seismic hazard due to earthquakes and volcanoes, securing our access to hydrocarbon resources and monitoring CO2 storage sites, all represent crucial issues for modern societies.

In this course, we will introduce parallel programming paradigms to the students in the context of applied mathematics.

Taking into account extreme events (heavy rainfalls, floods, etc.) is often crucial in the statistical approach to risk modeling.

Statistical learning is about the construction and study of systems that can automatically learn from data.

This lecture will link levelset modeling of biomechanical systems (e.g. immersed elastic membranes mechanics) with optimal transportation theory.

Understanding of fundamental notions in Machine Learning (inference, ERM and SRM principles, generalization bounds, classical learning models, unsupervised learning, semi-supervised learning.

Many industrial applications involve expensive computational codes which can take weeks or months to run. It is typical for weather prediction, in aerospace sector or in the civil engineering field.

When estimating parameters in a statistical model, sharp calibration is important to get optimal performances.

This lecture proposes modelling problems. The problems can be industrial or academic.

Optimal transport is an important field of mathematics that was originally introduced in the 1700’s by the French mathematician and engineer Gaspard Monge to solve the following very applied problem …

This lecture presents various useful applications, libraries and methods for software engineering related to applied mathematics.

This course is related to mathematical and statistical methods for forecasting in supervised learning.

This MSc course aims at presenting the fundamental concepts of Stochastic Calculus, and the way these concepts have been used in order to build models for applications to finance.

Wavelets are basis functions widely used in a large variety of fields: signal and image processing, numerical schemes for partial differential equations, scientific visualization

.js-id-MSCI
#### A mathematical analysis of positive discrimination mechanisms in multi-dimensions

#### Amélioration de la résolution spatiale d’images IRM : développement d’un algorithme de super-résolution

#### Censored demands estimation in vehicle sharing systems

#### Deep learning for patent selection

#### Fair online classification with partial feedback

#### Model preprocessing for stochastic optimization

#### Modeling and simulation of periodic inflatable materials

#### Online Algorithms for Fair Ad Auctions

#### Optimisation variationnelle pour l’amélioration de résolution en IRM

#### Uncertainty quantification in Stochastic Differential Equations and applications to Neurosciences

Polaris team INRIA / LIG Grenoble

Laboratoire LIP/IXXI, ENS Lyon

G-SCOP, Grenoble

C TEC, Grenoble

Polaris team INRIA / LIG Grenoble

G-SCOP Grenoble

INRIA Grenoble

Polaris team INRIA / LIG Grenoble

Laboratoire LIP/IXXI, ENS Lyon

Airsea and IPS teams, LJK Grenoble

.js-id-DS
#### A mathematical analysis of positive discrimination mechanisms in multi-dimensions

#### Amélioration de la résolution spatiale d’images IRM : développement d’un algorithme de super-résolution

#### Censored demands estimation in vehicle sharing systems

#### Deep learning for patent selection

#### Fair online classification with partial feedback

#### Model preprocessing for stochastic optimization

#### Modeling and simulation of periodic inflatable materials

#### Online Algorithms for Fair Ad Auctions

#### Optimisation variationnelle pour l’amélioration de résolution en IRM

#### Uncertainty quantification in Stochastic Differential Equations and applications to Neurosciences

Polaris team INRIA / LIG Grenoble

Laboratoire LIP/IXXI, ENS Lyon

G-SCOP, Grenoble

C TEC, Grenoble

Polaris team INRIA / LIG Grenoble

G-SCOP Grenoble

INRIA Grenoble

Polaris team INRIA / LIG Grenoble

Laboratoire LIP/IXXI, ENS Lyon

Airsea and IPS teams, LJK Grenoble

Heads of the program : Pierre Etoré and Edouard Oudet

Adminstrative contact : Carine Beaujolais and Emmnanuel Villemont

M2 SIAM : msiam2 (at) ensimag (dot) fr