90352 - MACHINE LEARNING FOR ECONOMISTS

Conoscenze e abilità da conseguire

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.

Contenuti

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

 

Testi/Bibliografia

• 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

Metodi didattici

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.

Modalità di verifica e valutazione dell'apprendimento

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.

 

Strumenti a supporto della didattica

Slides, lecture notes for the theory part.

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

Orario di ricevimento

Consulta il sito web di Matteo Barigozzi