HMMA308 - Statistical Learning (2018-2020)

ML intro and basics, mostly supervised and no deep learning.
Random forest
linear regression
PCA
clustering
Three level representations for 2D functions connected to sparse regression objective functions.

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