HMMA308 - Statistical Learning (2018-2020)

ML intro and basics, mostly supervised and no deep learning.
Random forest
linear regression
PCA
clustering

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

  12. Splines et GAM : Splines_GAM_slides.pdf

TPs

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

  1. perceptron_tp.pdf, tp_perceptron_source.py, tp_perceptron_script.py,

  2. arbres_tp.pdf, tp_arbres_source.py

  3. svm_tp.pdf, svm_tp_sources.zip,

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

More resources