Machine Learning & Econometrics
Course description and objectives
Do you feel lost in the random forests? Do you need some career boosting? Would you like to demystify magic words like cross-validation, bagging, shrinkage, 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 course on machine learning techniques.
These lectures have 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.
The course will present Machine Learning Techniques to econometricians. In particular, I will
present various concepts intensively used in the Machine Learning literature such as cross-validation, bootstrap, optimization routines;
describe and explain popular machine learning techniques such as random trees, random forests, boosting, neural nets and deep learning, and their natural extensions to time series analysis and causal inference.
This course was initially designed for the SIDE Summer School on Machine Learning.
Slides : pdf
Contents
Introduction
The two Cultures
Loss function and penalization
In-sample, out-sample and cross validation
Methods and Algorithms
Ridge and Lasso Regression
Classification and Regression Tree
Bagging and Random Forests
Boosting
Support Vector Machine
Neural Networks and Deep Learning
Topics in Econometrics
Interpretability
Causality