Here are some relevant information related to the Machine Learning lectures.
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)
[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)
2019 Innomech Master's program
The lectures and practical are planned on the Weeks 49,50 and 51.
Here is the correction of the last question of the practical : face_3D_cor.py.zip
The report should be sent to thierry.chateau_at_uca.fr. (replace _at_ by @).