Juri Marcucci, Bank of Italy


Arthur Charpentier (UQAM, Canada)

Emmanuel Flachaire (AMSE, France)


Course description:

Do you feel lost in the random forests? Do you need some career boosting? Would you like to demystify magic words like cross-validationbaggingshrinkage, etc? Or discover what is hidden behind wild acronyms like GAM, LASSO, GBM, etc. that you heard during that meeting or at the coffee machine or at that seminar with a fancy title? If so then you should consider attending this one-week intensive course on machine learning techniques.

These lectures has been conceived by econometricians for econometricians. The sessions proceed step by step, recalling the fundamental statistical concepts at the heart of the modern learning techniques. Their relative merits are illustrated by means of several case studies with real data.

Course Schedule:

Monday July 15

Session 1A (Flachaire): Introduction, Model Misspecification, Nonlinearities, Nonparametric Econometrics (kernels, splines and GAMs)
Session 1B (Charpentier): Loss Functions, Objective Functions and Penalty (quantile regression, LASSO, ridge)

Tuesday July 16

Session 2A (Flachaire): Cross Validation, Overfit, Bootstrap and Bagging)
Session 2B (Flchaire): Classification, part I: logistic regression, trees, forests

Wednesday July 17

Session 3A (Charpentier): Classification, part II: neural networks and deep learning, and Model Selection (ROC, AUC))
Session 3B: excursion

Thursday July 18

Session 4A (Charpentier & Flachaire): Group “Hands-On Classification” on Real Data)
Session 4B (Flachaire): Regression: boosting, regression trees and forests

Friday July 19

Session 5A (Charpentier & Flachaire): Group “Hands-On Regression” on Real Data)
Session 5B (Charpentier): Algorithmic and Optimization Issues, Extension of Machine Learning Techniques to Time Series

Saturday July 20

Session 6A (Charpentier): Causality with Machine Learning Algorithms)


For more information: Antonella Mallus  e-mail:

For administrative issues : Alessandra Picariello phone:+39 0512092637; e-mail: