90352 - Machine Learning For Economists

Course Unit Page

  • Teacher Matteo Barigozzi

  • Credits 6

  • SSD SECS-P/05

  • Teaching Mode Traditional lectures

  • Language English

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Economics (cod. 8408)

  • Course Timetable from Nov 10, 2021 to Dec 15, 2021

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2021/2022

Learning outcomes

At the end of the course the student will have a good understanding of the main tools used in machine learning. In particular, he/she: - understands and knows how to apply key aspects of machine and statistical learning, such as out-of-sample cross-validation, regularization and scalability - is familiar with the concepts of supervised learning, regression and classification - understands and can apply the main learning tools such as lasso and ridge regression, regression trees, boosting, bagging and random forests, support vector machines and neural nets. - The course will put special emphasis on empirical applications using the R software.

Course contents

  1. Introduction and Overview of Statistical Learning
  2. Resampling Methods: Cross-Validation and the Bootstrap
  3. Linear Model Selection and Regularization: Ridge Regression, the LASSO and Principal Components
  4. Moving Beyond Linearity: Regression Splines, Smoothing Splines and General Additive Models
  5. Tree-based Methods: CART, Bagging, Boosting and Random Forests
  6. Neural Networks
  7. Unsupervised Learning: 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
  • Lecture notes
  • Selected papers

Teaching methods

For each topic we will first introduce the relevant theory, and then move to its empirical application in the R language or Matlab (for factor models). Special emphasis will be placed on the economic interpretation of the results.

Assessment methods

Final project followed by an oral discussion.

Passing numerical grades are intended to match the following qualitative description:

18-23: sufficient
24-27: good
28-30: very good
30 cum laude: excellent.

 

Teaching tools

Slides, lecture notes for the theory part.

R packages and Matlab codes to discuss empirical analysis and replicate the results of a few papers.

Office hours

See the website of Matteo Barigozzi