Machine Learning and AI for Economics and Finance, 3rd Ed.

PSL and Dauphine summer school

As part of the special “transverse program” DATA of PSL, we organize the Eco/Finance AI summer school.

You want to learn:

  • How can AI interact with economics and finance?
  • How to build machine learning models from data?
  • What is the connection between causality and machine learning?
  • What can we learn from texts (Natural Language Processing)?

The goal of this summer school is to introduce recent and important tools from data science that can address issues in the fields of economics and finance. This week combines lectures and lab sessions to provide participants with the starter skills.

Important: Master and PhD students should check with their administration if this course can be validated within their programme.

Dates and location

Dates: November 21 to 25

Location: PariSanté campus, 10 rue d’Oradour-sur-Glane, 75015 Paris

Prerequisites

Knowledge of Python, NumPy and pandas

Pre-register to the Courses

Pre-registration is free but mandatory.

PSL students have priority if they pre-register before the 6th of June.

Lab sessions

Participants are expected to bring their own laptop for the lab sessions. Those who want to setup their computer can install anaconda. This Python distribution comes with all the necessary tools. Google Colab with a Google account can be used also.

Schedule

The expected program is described below, the morning sessions are from 9am to 12pm and the afternoon sessions from 2pm to 5pm

Day 1:

  • Morning: Foundations of Machine Learning, Fabrice Riva (Dauphine Université-PSL)
  • Afternoon: Portfolio simplification with K-means, Fabrice Riva (Dauphine Université-PSL)

Day 2: Artificial intelligence in Finance

Day 3: Natural Language Processing

Course and Lab sessions by Matthieu Labeau.

Day 4:

  • Morning: Arthur Thomas, Backpropagation and automatic differentiation
  • Afternoon: Grégoire Pacreau, Graph neural networks

Day 5:

  • From 10 to 13h: Investing through Economic Cycles with Machine Learning Algorithms, Sylvain Benoit (Dauphine Université-PSL)

Many courses will include lab parts. All the code will rely on Python. If you want to setup your computer, you can install anaconda. This Python distribution comes with all the necessary tools. You can also use Google Colab with a Google account.

Organizers