ECONOMIA E IMPRESAData ScienceAnno accademico 2023/2024

9793875 - DATA ANALYSIS AND STATISTICAL LEARNING
Modulo STATISTICAL LEARNING

Docente: Salvatore INGRASSIA

Risultati di apprendimento attesi

Il modulo fornisce conoscenze su: i) il problema dell'apprendimento statistico del modello di apprendimento da dati empirici; ii) principali tecnici di apprendimento statistico per regressione e classificazione. 

Modalità di svolgimento dell'insegnamento

Lectures and practical data modeling in R.

Prerequisiti richiesti

Main topics in algebra, calculus, geometry, probability (at bachelor level).

Frequenza lezioni

In person.

Contenuti del corso

Statistical Learning. Estimation of dependences based on empirical data. Supervised and Unsupervised Learning. Regression and Classification problems. Parametric and non-parametric models. Assessing Model Accuracy.

Linear Regression. Simple linear regression. Multiple linear regression. Least squares criterion and parameter estimation. Assessing the accuracy of the coefficient estimates and of the model. Use of qualitative predictors. Extension of the linear model and non-linear relationships.

Statistical Learning. Estimation of dependences based on empirical data. Supervised and Unsupervised Learning. Regression and Classification problems. Parametric and non-parametric models. Assessing Model Accuracy.

Linear Regression. Simple linear regression. Multiple linear regression. Least squares criterion and parameter estimation. Assessing the accuracy of the coefficient estimates and of the model. Use of qualitative predictors. Extension of the linear model and non-linear relationships.

Classification. Logistic regression; parameter estimation. Linear and quadratic discriminant analysis.

Resampling methods. Cross-validation, Bootstrap.

Linear model selection and regularization. Subset selection. Shrinkage methods. Dimension Reduction Methods, Modeling in high dimensions.

Tree-based Methods. Regression Trees and Classification Trees. Bagging, Random Forest, Boosting

Testi di riferimento

1. James G., Witten D., Hastie T., Tibshirani R. (2023). An Introduction to Statistical Learning with Applications in R, 2nd Edition, Springer, New York, https://hastie.su.domains/ISLR2/ISLRv2_corrected_June_2023.pdf

2. Hastie T., Tibshirani R., Friedman (2008). The Elements of Statistical Learning, Springer, New York

3. Course notes


AutoreTitoloEditoreAnnoISBN
James G., Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning with Applications in RSpringer2021
Hastie T., Tibshirani R., Friedman The Elements of Statistical LearningSpringer2008

Programmazione del corso

 ArgomentiRiferimenti testi
1Basics of statistical learning.Textbook n.1, chap. 1
2Linear RegressionTextbook n.1, chap. 2
3ClassificationTextbook n.1, par. 4.1-4.5
4Resampling methodsTextbook n.1, chap.5
5Linear Model Selection and RegularizationTextbook n.1, chap. 6
6Tree-based methodsTextbook n.1, chap. 8

Verifica dell'apprendimento

Modalità di verifica dell'apprendimento

The evaluation will be based on a data analysis report  and oral exam.

Esempi di domande e/o esercizi frequenti

See the course content.

English version