Orateur
Prof.
Alexandre Gramfort
(INRIA)
Description
In this lecture I will cover the various families of problems that machine learning can solve. Using examples, primarily on images, I will present various linear and non-linear dimensionality reduction methods (PCA,NMF, T-SNE), I will motivate the use of certain clustering techniques (K-means, DBSCAN) and I will then explain which families of methods exist for building predictive models (linear methods, trees, nearest neighbors). I will insist on the computational aspects explaining the differences between batch and online learning which is necessary when data become too big to fit in RAM. This talk with be a live demo using the scikit-learn software.
Auteur principal
Prof.
Alexandre Gramfort
(INRIA)
