Theme
This course given in English in the masters économie internationale et développement,
digital economics and quantitative economic analysis.
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
Privacy
- slides on privacy: in English (last update: 06/07/2020)
Supervised learning models
- slides on decision trees: in English (R code). Last update: 22/01/2020.
- slides on optimal models and naive bayes: in
English. Last update: 06/01/2020.
- slides on support vector machines and kernel methods: in
English (R code). Last update: 06/01/2020.
- slides on ensemble methods (including random forests and
boosting): in English (R code). Last update: 26/02/2020.
Tools for supervised learning
- slides on cross-validation and other resampling methods: in English
(with R code), updated on 06/01/2020, and in French with more content (R code), updated on 10/01/2020.
- slides on imbalanced data: in French
(R code), updated on 20/04/2020.
Unsupervised learning
- slides on clustering: English (R code). Last update: 18/10/2019.
- slides on outlier detection: English (R code). Last update: 16/06/2020.
Theory
- slides on empirical risk minimization: in
English (last update: 01/24/2018)
- slides on regularization and capacity control: in
English (last update: 01/24/2018)
- a more advanced and more thorough presentation of the same concepts are
available in my slides on learning theory: in
French and in English
Exercises and Labs
Introduction
Decision trees
Recommended reading/viewing
Full course
Tom Mitchell's and Nina Balcan's machine learning course:
Selected topics