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public:res-ens:dmsml:dmsml [2018/12/19 11:56] – external edit 127.0.0.1public:res-ens:dmsml:dmsml [2023/07/20 10:00] (current) – external edit 127.0.0.1
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 == Slides == == Slides ==
  
-{{:res-ens:dmsml:intro.pdf|}}+{{:public:res-ens:dmsml:intro.pdf|}}
  
  
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 Download the following data:  Download the following data: 
-{{:res-ens:dmsml:data.zip|}}+{{:public:res-ens:dmsml:data.zip|}}
  
 This archive contains data for both regression and classification exercises. The first assignment consists in preparing and visualizing data. This archive contains data for both regression and classification exercises. The first assignment consists in preparing and visualizing data.
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 The classification data are images from three types of flowers. You must extract features from these images called mean normalized colors. The classification data are images from three types of flowers. You must extract features from these images called mean normalized colors.
-{{ :res-ens:dmsml:normalizedcolor.png?direct |}}+{{ :public:res-ens:dmsml:normalizedcolor.png?direct |}}
  
 Then, plot the features, using one separate color per type of flowers. You should obtain a plot looking like this: Then, plot the features, using one separate color per type of flowers. You should obtain a plot looking like this:
  
-{{ :res-ens:dmsml:data_classif.png?direct |}}+{{ :public:res-ens:dmsml:data_classif.png?direct |}}
  
 Your goal, for these labs, is to partition the space into areas of influence for each class, using the techniques learned in class. Your goal, for these labs, is to partition the space into areas of influence for each class, using the techniques learned in class.
-In other terms, you will classify each point in space using the classifiers we have studied. Download a skeleton for this assignment here: {{:res-ens:dmsml:skeleton.txt|}}+In other terms, you will classify each point in space using the classifiers we have studied. Download a skeleton for this assignment here: {{:public:res-ens:dmsml:skeleton.txt|}}
  
 Here is an example of what you should obtain using a K-nearest neighbors classifier with K = 1: Here is an example of what you should obtain using a K-nearest neighbors classifier with K = 1:
-{{ :res-ens:dmsml:knn-classif.png?direct |}}+{{ :public:res-ens:dmsml:knn-classif.png?direct |}}
  
 Try different values of K and comment the results. Try different values of K and comment the results.
public/res-ens/dmsml/dmsml.1545216997.txt.gz · Last modified: 2023/07/20 09:59 (external edit)