High Dimensional and Deep Learning (HDDL)

Description

Objectifs

At the end of this module, the student will have understood and be able to explain (main concepts):

-The aggregation of learning methods via boosting algorithms
-Using deep learning methods for classification in high dimension
-Classification of signals or images
-Estimation of the prediction error
-Dimension reduction by projection onto orthonormal bases
-Anomaly detection algorithms
-Recurent neural networks for time series forcasting

The student will be able to:

-Implement and optimize boosting algorithms on datasets
-Fit a deep neural network for signal or image classification
-Apply anomaly detection algorithms
-Use recurent neural networks for time series forcasting
-Implement deep learning methods in high dimension on real data sets with the software R or Python's libraries.

Pré-requis

Statistical modelling
Machine Learning
Software for statistics (R,Python)

Évaluation

L’évaluation des acquis d’apprentissage est réalisée en continu tout le long du semestre. En fonction des enseignements, elle peut prendre différentes formes : examen écrit, oral, compte-rendu, rapport écrit, évaluation par les pairs…