Theme

This course given in English in the master économie internationale et développement. It gives a full coverage of modern machine learning.

Lectures notes/slides

Please note that the R scripts below have been extracted automatically from the knitr sources of the slides. They must be adapted to run properly: paths to data files must be modified and the opt_chunk related code must be removed. The code is developed under GNU/Linux and uses frequently the doMC package which is not available under MS Windows. It should be replaced by the doParallel package (and the code should be adapted).

Introduction

  • slides on an introduction to machine learning: in English (R code)
  • a short introduction to computational complexity: in English

Privacy

Supervised learning models

  • slides on decision trees: in English (R code)
  • slides on optimal models and naive bayes: in English
  • slides on support vector machines and kernel methods: in English (R code)
  • slides on ensemble methods (including random forests and boosting): in English (R code)

Tools for supervised learning

Unsupervised learning

Theory

  • slides on empirical risk minimization: in English
  • slides on regularization and capacity control: in English
  • a more advanced and more thorough presentation of the same concepts are available in my slides on learning theory: in French and in English

Recommended reading/viewing

Full course

Tom Mitchell's and Nina Balcan's machine learning course:

Selected topics