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)
DU Data Scientist Lecture on Deep Visual Tracking
Link to MultiObject Tracking Google Colab