Degrees incorporating this pedagocial element :
Description
A new course that will follow the one in the 2nd semester of the first year, but that can also be chosen by students with previous experience in the field. A detailed description will be posted later, in the meantime look at the corresponding UE of the first year.
This course introduces the main deep learning methods relevant for Earth Science applications, where the processing of time series and images (sometimes noisy, incomplete) and forecasting are routine problems. This includes for example Convolutional Neural Networks, Recurrent Neural Networks, and Generative Networks.
Pre-requisites: Ideally: Introduction to Machine Learning in Earth Sciences, course from the first year of STPE Master. If not: good knowledge in Python, basic notions in differential calculation and linear algebra.
Languages: English, French
Courses offered by :
In brief
Credits : 3Number of hours
- Lectures (CM) & Teaching Unit (UE) : 12h
- Practical work (TP) : 15h
Language(s) : English
