- Docente: Matteo Barigozzi
- Crediti formativi: 6
- SSD: SECS-P/05
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
- Corso: Laurea Magistrale in Economics (cod. 8408)
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
- Introduction and Overview of Statistical Learning
- Resampling Methods: Cross-Validation and the Bootstrap
- Linear Model Selection and Regularization: Ridge Regression, the LASSO and Principal Components
- Moving Beyond Linearity: Regression Splines, Smoothing Splines and General Additive Models
- Tree-based Methods: CART, Bagging, Boosting and Random Forests
- Neural Networks
- 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
SDGs
L'insegnamento contribuisce al perseguimento degli Obiettivi di Sviluppo Sostenibile dell'Agenda 2030 dell'ONU.