• Voir la page en français

    In brief

  • ECTS credits : 5
  • Code : N8EN07


Optimisation 2:
Machine learning application often lead to optimisation problems of a composite nature: a typical fit-to-data term is
penalized so as to enforce some geometrical properties in the solution. Typical properties include sparcity, low rank
in matrices. Such problems are often non-differentiable but convex. We review the most popular sub-gradient based methods
for solving such problems, insisting on the convergence properties and the complexity of such methods. We will also focus
on efficient implementation of such methods on image processing applications. Finally, we will develop in the SPARK software
a movie recommendation system.

Statistique 2:
In this course, the basic regression model is introduced along with its applications and extensions (generalized linear models
especially logistic regression).  Linear models provide an indispensable basis for later approaches to more modern methods used in big data.
Algorithms will be used in practical works with R to automatically select predictors and a procedure to evaluate the models will be detailled.


First order methods in optimization, Amir Beck, SIAM
Convex Optimization: Algorithms and Complexity, Sebastian Bubeck
Régression avec R, Cornillon & Matzner-Lober, Springer
An R companion to applied regression, Fox & Weisberg, Sage





The National Institute of Electrical engineering, Electronics, Computer science,Fluid mechanics & Telecommunications and Networks

2, rue Charles Camichel - BP 7122
31071 Toulouse Cedex 7, France

+33 (0)5 34 32 20 00


  • Logo MENESR
  • Logo UTFTMP
  • Logo INP
  • Logo INPT
  • Logo Mines télécoms
  • Logo CTI
  • Logo CDEFI
  • Logo midisup