Master 2 in Mathematics - Learning and Algorithms track
The Master 2 Learning and Algorithms track (M2A) offers a dual training in Mathematics and Computer Science, focusing on data science and artificial intelligence, with a particular emphasis on statistical learning and deep learning. The training provided is both :
- theoretical, through teaching consisting of lectures, tutorials, practical work and projects
- operational, thanks to an internship in the second semester, and through direct contact with companies and laboratories proposing concrete machine learning problems.
Courses Taught in English - Master 2 in Mathematics - 2A
2nd year Master - 1st Semester - 3 ECTS - English Level: Scientific English (no test required)
Brief Description
This course presents convergence results for many sequential algorithms of Machine Learning, both in the deterministic and random setting. It will be shown that sequential learning provides adaptive and robust solutions to many convex optimization problems, with and without constraints. Convergence of algorithms will be illustrated with R and Python on the MNIST dataset.
Prerequisites
Fundamentals of probability and statistics, scientific computation in Python or R.
Contact
Olivier Wintenberger (olivier.wintenberger@sorbonne-universite.fr)
2nd year Master - 2nd Semester - 3 ECTS - English Level: Scientific English (no test required)
Brief Description
This course presents how to use neural networks for adaptive numerical approximation.
Topics covered:
- Functions that can be represented by neural networks
- Elementary proofs of Cybenko's theorem
- The Takagi function
- Construction of datasets and curse of dimension
- Interpretation of stochastic gradient algorithms in the form of ordinary differential equations
- Applications to problems from scientific computing for CFD in relation with image classification
Illustration with some software.
Prerequisites
Fundamentals of analysis and interest in programming.
Contact
Bruno Desprès (bruno.despres@sorbonne-universite.fr)
2nd year Master - 2nd Semester - 3 ECTS - English Level: Scientific English (no test required)
Brief Description
The goal of this course is twofold: on the one hand, to discover the real challenges of basic biology and medicine where statistical learning is already successfully used; on the other hand, to acquire the basics for modeling complex medical data.
Prerequisites
Fundamentals of probability and statistics, linear algebra, Python.
Contact
Xavier Tannier (xavier.tannier@sorbonne-universite.fr)
2nd year Master - 2nd Semester - 1,5 ECTS - English Level: Scientific English (no test required)
Brief Description
This lecture provides an overview of PAC-Bayesian theory, starting from statistical learning theory (generalization bounds and oracle inequalities) and covering algorithmic developments by variational inference, up to recent PAC-Bayesian analyses of generalization properties of deep neural networks.
Prerequisites
Fundamentals of probability and statistics.
Contact
Badr-Eddine Chérief-Abdellatif (badr-eddine.cherief-abdellatif@sorbonne-universite.fr)
2nd year Master - 2nd Semester - 3 ECTS - English Level: Scientific English (no test required)
Brief Description
The objective of this course is twofold: to illustrate the processing of high-dimensional data when data is missing (through the prism of compressed acquisition and matrix completion), and to acquire the basics of convex optimization. These two topics, which will be addressed in concert as they are closely related, open the way to many other statistical learning domains and problems encountered in data science.
Prerequisites
Fundamentals of probability and statistics, linear algebra, Python.
Contact
Claire Boyer (claire.boyer@sorbonne-universite.fr)
2nd year Master - 2nd Semester - 3 ECTS - English Level: Scientific English (no test required)
Brief Description
This course will attempt to provide an overview of the latest mathematical trends in the machine learning and statistical learning community.
Topics covered:
- Approximation theory for neural networks
- VC dimension for neural networks
- Minimal bounds for regression with neural networks
- GANs
- Implicit bias in gradient descent
- Interpolation & benign overfitting
- Privacy
Prerequisites
Fundamentals of probability and statistics, linear algebra.
Contact
Eddie Aamari (eddie.aamari@sorbonne-universite.fr)
2nd year Master - 2nd Semester - 3 ECTS - English Level: Scientific English (no test required)
Brief Description
Data can often be represented by point clouds in high-dimensional spaces. In practice, it is found that these points are not uniformly distributed in the surrounding space: they are located near non-linear structures of smaller dimension, such as curves or surfaces, which are interesting to understand. Geometric inference, also called topological data analysis, is a recent field consisting in the study of statistical aspects associated with the geometry of data. This course aims to provide an introduction to this rapidly growing field.
Prerequisites
Fundamentals of probability and statistics.
Contact
Eddie Aamari (eddie.aamari@sorbonne-universite.fr)