This course was devided in two parts. The aim of the first oen was to introduce the foundations of Machine Learning : data - data transformation - model and genralities - complexity of a model - cross validation and so on...
The second part was dedicated to bayesian classification illsutrated with the k-nearest neighbors algorithm and its variants. It was also the occasion to talk about the curse of dimensionality in Machine Learning.

The documents below were made by Marc Sebban.

Courses

• Intro Part I
• Intro Part II