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Innovative methods in data analysis

May 7, 2024

Training morning

8.45am to 9am: Welcome greetings

9am to 12.30pm (including a break) : Introduction to Machine Learning

This course is a practical introduction to the fundamental concepts of machine learning. Using real data, we will build various predictive models (classification, regression), from simple linear models to random forests. We will detail the various stages involved in building a model: data preparation, learning and evaluation. The examples will use the scikit-learn library, a reference tool for the Python language.

The course is accessible without any knowledge of machine learning. Basic knowledge of the python language is desirable, but not mandatory (variable and function definitions, importing modules). Some experience with the numpy and pandas libraries is also a plus.

Speaker: Mathieu Bernard (CNRS research ingineer, Economix)

12.30pm to 2pm: Lunch

Afternoon of presentations

2pm to 3pm : Key-note speaker Emmanuel Flachaire (Aix-Marseille University)

Interpretability and causality in machine learning

3pm to 3:45pm  :  Pierre Beaucoral (CERDI, Université Clermont Auvergne)

Leveraging Natural Langage processing techniques for a better classification of development projects: a case study using OECD CRS dataset

3:45pm to 4pm  :  Pause

4pm to 4:45pm  :  Johannes van der Pol (Copernicus Institute of Sustainable Development, Utrecht University)

One patent to rule them all ? The scope of multi-standard essentials explored

4:45pm to 5:30pm  :  Rim Bahroun (EconomiX, Université Paris Nanterre)

Large Langage Models for advanced text processing: application in economics

Join us by visioconference for free (only on afternoon) !