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.
Convolutional Neural Networks (CNNs).
Recurrent Neural Networks.
Adversarial and generative networks.
IBM Data Science tools.

 

Planning & teachers:
18 septembre    13h30   2h CM     2h Lab    Lionel Fillatre
25 septembre    13h30   2h CM     2h Lab    Pierre Alliez + Gaetan Bahl
02 octobre    13h30   2h CM     2h Lab    Florent Lafarge + Gaetan Bahl
09 octobre    13h30   2h CM     2h Lab    Pierre Alliez + Gaetan Bahl
16 octobre    16-19h    Yann Gouedo
23 octobre    16-19h    Yann Gouedo
30 octobre    17-19h    Yann Gouedo
06 novembre    exam    Pierre Alliez

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