# Latest news

# Tracks in semester 9: 2021-2022

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.

Academic courses:

# Semester 10: 2020-2021

# Refresher courses

Refresher courses can be followed at the beginning of semester 9, to be chosen among:

These refresher courses do not count in the total of 30 required ECTS.

## Modelling, Scientific Computing and Image analysis (MSCI)

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 mathemetics, modelling and computing in wide areas in the industry.

## Data Science (DS)

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, Probability and Statistics, 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 Probability and Statistics. 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).

** Important note in 2021-2022 :** the two couples of courses

(1) Non-smooth Convex Optimization Methods + (2) Efficient methods in optimization

(1) Model exploration for approximation of complex, high-dimensional problems + (2) Inverse problem and data assimilation : variational and Bayesian approaches

are thought so that (2) follows (1) and it makes sense to take (1)+(2) to have a deep view on the subject. Students are not forced to take (1)+(2), they can take for example just (1). But note that it will be difficult to follow (2), without having taken (1).

** More information about the interplay of the courses here: ** TO BE ANNOUNCED

Proposed courses:

Global list of courses

## Advice and testimonials (Alumni)