Optimisation 2
- Code : N8EN07A
In brief
Objectives
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.
Needed prerequisite
Basic course on linear algebra, Basic algorithms for unconstrained optimisation
Session 1 ou session unique - Contrôle des connaissances
| Modalité | Nature | Coefficient | Remarques |
|---|---|---|---|
| CT (contrôle terminal) | Oral/Ecrit | 50% | Examn Optimisation 2 |
| CC (contrôle continu) | Travaux Pratiques | 50% | TP-Optimisation 2 |
Session 2 - Contrôle des connaissances
| Modalité | Nature | Coefficient | Remarques |
|---|---|---|---|
| CT (contrôle terminal) | Oral/Ecrit | 50% | Examn Optimisation 2 |
| CC (contrôle continu) | Travaux Pratiques | 50% | TP-Optimisation 2 |
