9793875 - DATA ANALYSIS AND STATISTICAL LEARNING
Modulo STATISTICAL LEARNING

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

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