This course is in French, and deals with Machine Learning, mostly supervised learning.

Syllabus

syllabus_hmma308.pdf

Projets

Projets_ML.pdf

Cours

1. Intro Machine Learning: IntroML_slides.pdf

2. Régression logistique/LDA: RegressionLogistique_slides.pdf

3. Validation Croisée: CrossValidation_slides.pdf

4. SVD: SVD_slides.pdf, SVD.ipynb

5. PCA: PCA_slides.pdf, PCA.ipynb

6. Ridge: Ridge_slides.pdf, Ridge.ipynb

7. Arbres : arbres_slides.pdf

8. Bagging et forêts aléatoires : ForetsAleatoires_slides.pdf

9. Lasso: Lasso_slides.pdf, Lasso_fr.ipynb, functions_Lasso.py, prox_collection.py

10. SVM : svm_slides.pdf

11. Clustering : clustering_slides.pdf

TPs

1. knn_tp.pdf, tp_knn_source.py, tp_knn_script.py, tp_knn_script_corr.py, knn_tp_corr.pdf

2. perceptron_tp.pdf, tp_perceptron_source.py, tp_perceptron_script.py, TP_perceptron.ipynb

3. arbres_tp.pdf, tp_arbres_source.py

4. svm_tp.pdf, svm_tp_sources.zip,

5. clustering_tp.pdf, kmeans.py, gap.py, china.jpg

More resources

• The end of the average

• Le fléau de la dimension (Python) inspiré par Le fléau de la dimension (R)

• Leakage (fr: fuite) example : https://twitter.com/acidflask/status/1259531004500488192

• The Literary Digest poll : https://amsi.org.au/ESA_Senior_Years/SeniorTopic4/4b/4b_2content_4.html

• Survivorship bias