DATA ANALYSIS AND STATISTICAL LEARNINGModulo STATISTICAL LEARNING
Academic Year 2023/2024 - Docente: Salvatore INGRASSIARisultati di apprendimento attesi
Course Structure
Required Prerequisites
Attendance of Lessons
Detailed Course Content
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
Textbook Information
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
| Autore | Titolo | Editore | Year | ISBN |
|---|---|---|---|---|
| James G., Witten D., Hastie T., Tibshirani R. | An Introduction to Statistical Learning with Applications in R | Springer | 2021 | |
| Hastie T., Tibshirani R., Friedman | The Elements of Statistical Learning | Springer | 2008 |
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Basics of statistical learning. | Textbook n.1, chap. 1 |
| 2 | Linear Regression | Textbook n.1, chap. 2 |
| 3 | Classification | Textbook n.1, par. 4.1-4.5 |
| 4 | Resampling methods | Textbook n.1, chap.5 |
| 5 | Linear Model Selection and Regularization | Textbook n.1, chap. 6 |
| 6 | Tree-based methods | Textbook n.1, chap. 8 |