Here are some relevant information related to the Machine Learning lectures. 

Content:

Introduction: before beginning (download slides)

[L1] Lecture 1: Introduction to machine learning: (download handout)

[L2] Lecture 2: Naïve Bayes classifier: (download handout)

[L3] Lecture 3: Gaussian distributions:  (download handout)

[L4] Lecture 4: Non parametric classifiers: (download handout)

 

Several practicals are associated to the lectures:

[P0]: Practical 0 : Introduction to Machine Learning (overfitting and model complexity): (download) (google colab 1/2, 2/2)

[P1]: Practical 1 : Normal distributions and Naive Bayes Classifiers (related to L2 and L3): (download)

[P2]: Practical 2 : Non Parametric Classifiers (related to L2 and L3): (download)

[P3]: Practical 3:  Estimation of 3D distance on faces using Machine Learning technics : (download)

 

Here is a tuto. for connecting to the VM Mecatronique (download)

 

Here is a python script using sklearn to compare classifiers  (download)

  

Attachments

studentsML P .zip [5.2Mb]
Uploaded Thursday, 13 September 2018 by Super Utilisateur
TD .pdf [966.01Kb]
Uploaded Tuesday, 18 September 2018 by Super Utilisateur
201809180505 ML handout.pdf [5.17Mb]
Uploaded Tuesday, 18 September 2018 by Super Utilisateur
201810011210 ML .pdf [3.82Mb]
Uploaded Monday, 01 October 2018 by Super Utilisateur
201810011213 studentsML P .zip [43.33Mb]
Uploaded Monday, 01 October 2018 by Super Utilisateur
mecatroniquehowto.pdf [311.18Kb]
Uploaded Tuesday, 08 October 2019 by Super Utilisateur
ML1P0.zip [3.23Kb]
Uploaded Wednesday, 04 December 2019 by Super Utilisateur
201912110727 ML2.pdf [4Mb]
Uploaded Wednesday, 11 December 2019 by Super Utilisateur
ML3.pdf [4.39Mb]
Uploaded Wednesday, 11 December 2019 by Super Utilisateur
face 3D1 cor py.zip [2.37Kb]
Uploaded Friday, 27 December 2019 by Super Utilisateur