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Teaching 2017/2018



UW: Robust statistics (STAT 593)

Syllabus for this course: syllabus.pdf
Useful code (for plotting): utils.py

1) Introduction: IntroRobustness.pdf; IntroRobustness.ipynb; LeveragePoints.ipynb
2) Equivariance / Breakdown Point: Equivariance_BP.pdf
3) Depth / High Breakdown points: Depth.pdf
4) Location and scale : LocationScale.pdf; Huber_display.ipynb
5) L-estimates and influence functions : L-Estimates.pdf


M2MO : Statistical Learning

Master 2

Documents for this course might be found on S. Clémençon's webpage.

Tutorial classes:
Tutorial 1: learning_td1.pdf
Tutorial 2: learning_td2.pdf
Tutorial 3: learning_td3.pdf



SD204

TÉLÉCOM-ParisTech


1) Introduction: LeastSquare_1D_en.pdf; LeastSquare_1D_en.ipynb
2) Least squares : LeastSquare_Def_en.pdf; LeastSquare_1D_fr.ipynb
3) Least squares (properties) : LeastSquare_Prop_fr.pdf;
4) SVD: SVD_en.pdf (in French); SVD.ipynb
6) PCA: PCA_en.pdf (in French); PCA.ipynb
7) IntroIC: IntroIC_en.pdf (in French); IntroIC.ipynb
8) Bootstrap: Bootstrap_en.pdf;
9) IntroTests: IntroTests_en.pdf; forward_variable_selection.ipynb
10) Ridge: Ridge_en.pdf; Ridge_en.ipynb
11) Lasso: Lasso_en.pdf; Lasso_fr.ipynb;functions_Lasso.py; prox_collection.py
12) Coordinate descent: CD_en.pdf (in French); CD_en.ipynb
13) GLM: GLM_en.pdf (in French); GLM_fr.ipynb
14) Generalization: Generalization_en.pdf; Generalization_fr.ipynb
15) Categorical variables: CategoricalVariables.ipynb













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