Prof. Paola Bortot

• Additional material (scientific papers, tutorials, Python notebooks) will be provided during classes.

Argomenti del programma: The following topics will be covered: Introduction and Overview of Statistical Learning Linear Regression as a Prediction Tool Moving Beyond linear regression: K-Nearest Neighbors. Classification: K-Nearest Neighbors, Logistic Regression, misclassification rate, ROC curve, AUC.

Argomenti del programma: The course program is organized in four parts as described below. 1. Exploratory data analyis Graphical tools for data analysis and presentation. Frequency tables. Frequency distributions. Summary measures of position and dispersion. Two-way contingency tables. Joint, marginal and conditional distributions. Independence and Association. Covariance and correlation. 2.

• Additional material (scientific papers, tutorials, Python notebooks) will be provided during classes.

Argomenti del programma: The following topics will be covered: Introduction and Overview of Statistical Learning Linear Regression as a Prediction Tool Moving Beyond linear regression: K-Nearest Neighbors. Classification: K-Nearest Neighbors, Logistic Regression, misclassification rate, ROC curve, AUC.