The aim of this course is to give the fundamentals of machine learning, with a particular emphasis on how learning algorithms are constructed.
Each course is divided in two parts of 1h30 : a course part, where we describe a method, and another 1h30 part where either some details about the construction of the method, or a practical session using this method is provided.
All practical sessions are done using python in the jupyter notebook, with the sklearn library.
Always come with your laptop, with the anaconda distribution installed on it (or any other distribution shipped with python + jupyter + sklearn.
- Teaching coordinator: Erwan Scornet