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IML
Supervised Learning
Classwork Linear SVM
Classwork Linear SVM
Classwork Linear SVM
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ClassworkLinearSVM.pdf
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◄ Linear SVM
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Forum
Slides - Introduction to ML
Labwork - Introduction to classification approach
Due Labwork - Introduction to classification approach
Slides - Data Visualization
Labwork - Dataviz
Dataset - ais.csv
Dataset - USPS
Due Labwork - Data Visualization
Slides - Clustering Methods
Labwork - Clustering Methods
utility.py
starter_clustering.py
Dataset george.mat
Image seaforest_small.jpeg
Due Labwork Clustering
Unconstrained optimization
Labwork - Unconstrained Optimization
Due Labwork Unconstrained Optimization
Constrained Optimization: an introduction
Labwork - Convex Constrained Optimization
utility.py
maisons.csv
Due Labwork - Constrained Optimization
Advice for Applying Machine Learning de Andrew Ng (Stanford)
Bayesian Decision Theory
Classwork - Bayesian Decision Theory
Practice Session: Bayes's Decision Theory
Script utility_bayes.py
Data astrodata.mat
Dataset cat_and_grass.zip
Due Practice Session: Bayes Decision
Logistic Regression
Practice Session - Logistic Regression
Dataset: spambase.zip
Dataset Mnist.zip
Due Practice Session Logistic Regression
Linear SVM
Python Code to compute W and b (for SVM on your own)
Practical Session: Linear SVM
Script utility_svm.py
Due Linear SVM Labwork
Kernel SVM
Practice Session : Kernel SVM
Due Practice Session Work : Kernel SVM
Model Selection
Practice Model Selection
Data Splice
Splice test
Due Model Selection
TP Graph Kernels
Datasets
Challenge
Due Challenge
Examen - Partie 1 (clustering)
Corrigé indicatif - Clustering
Examen
Examen
Projets DM 2005 2006
Projets 2006/2007
Examen 2006-2007
Examen 2007-2008
Examen 2009-2010
Examen 2010-2011
Examen 2011-2012
Examen 2012-2013
Examen théorique 2014-2015
Examen Pratique 2015
Examen Théorique
Examen Pratique
Données
Examen théorique
Examen pratique
Donnees
Examen théorique
Examen pratique
Donnees
Examen théorique
Examen pratique
Donnees
Dépôt Examen machine - 2023-2024
Python Code to compute W and b (for SVM on your own) ►