Skip to content

This repo contains the work I did for the 'Optimization for Data Science' course at Ecole Polytechnique (X). This includes 4 labs and the final project.

Notifications You must be signed in to change notification settings

ralphmouawad/Optimization-for-Machine-Learning

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 

Repository files navigation

I - Four labs:

  • Proximal Algorithms for Non-smooth optimization (PGD, Accelerated PGD)
  • Stochastic Optimization (SGD, SGDA, SVRG, SAG)
  • Coordinate based algorithms (Proximal CD)
  • Quasi Newton methods (DFP, BFGS, L-BFGS)

II - Final Project: This includes mathematical derivations and implmenting optimization algorithms to solve the quantile regression problem with a smoothed pinball loss and (Non-)smooth penalties. Algorithms include:

  • Proximal Methods
  • Coordinate methods
  • Regular gradient descent
  • L-BFGS quasi newton
  • L1 and L2 proximal operators.

About

This repo contains the work I did for the 'Optimization for Data Science' course at Ecole Polytechnique (X). This includes 4 labs and the final project.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published