B2029 - INTRODUCTION TO MACHINE LEARNING FOR ECONOMISTS

Learning outcomes

The aim of the course is to provide the student with an introduction to the principles and methods at the core of Machine Learning (ML). At the end of the course, the student will have a working knowledge of supervised and unsupervised learning methods. Students will be familiar with some of the main ML algorithms and tools, how to evaluate their performance, and when to apply them to address questions of interest to economists. Students will be able to implement ML techniques in several empirical applications using the R software.

Course contents

1. Introduction to Statistical Learning

 

2. Regression, Classification, Resampling Methods

 

3. Supervised Learning I: Linear Models

• Linear Model Selection and Regularization (LASSO and Ridge regression)

• Bias/Variance Trade-off, Overfitting, Validation

 

4. Supervised Learning II: Nonlinear Models

• Tree-based methods (Trees, Random Forests, Bagging, Boosting)

• Support Vector Machines and Neural Networks

 

5. Unsupervised Learning:

• Clustering Analysis: K-means and Hierarchical Clustering

• Principal Component Analysis and Dynamic Factor Models

 

Readings/Bibliography

- G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning with Applications in R, 2nd Edition, 2021

- T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, 2nd Edition, 2009

- S. Athey and G. Imbens, Machine Learning Methods That Economists Should Know About (ArXiv, 2019; ARE, 2022)

- Other selected papers

 

Teaching methods

For each topic, we will cover the theory, along with relevant applications using the R software. Students may be asked to present material to lead discussion on some topics.

 

Assessment methods

Problem sets, final written exam, and/or development of individual or group projects.

The grading scale is the following:

<18: Fail

18-23: Sufficient

24-27: Good

28-29: Very good

30: Excellent

30 cum laude: Outstanding

Teaching tools

Dedicated page on the VIRTUALE platform containing:

· Lectures slides 

· Selected papers

· R packages and codes

· Example exercises

Office hours

See the website of Silvia Sarpietro