Course on Data Science

This course offers an introduction to data science as well as various software tools. It provides a comprehensive presentation of neural networks: deep, convolutional, recurrent, adversarial and generative. It also provides an introduction to the tools routinely used by data analysis practitioners. An important part of the course is devoted to practical case studies on computers, using Jupiter notebooks. More specifically, we will study the categorization of images, semantic segmentation of images and speech recognition. Part of the evaluation will take the form of a Kaggle challenge.

Syllabus:
Introduction: methodological framework, introduction to Kaggle and Python Notebooks (Lionel Fillatre)
Convolutional Neural Networks (CNNs) (Pierre Alliez)
Recurrent Neural Networks (Gaétan Bahl)
Adversarial and generative networks (Gaétan Bahl)
IBM Data Science tools (Yann Gouedo)

 

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