Statistical Learning For Healthcare Data – Politecnico di Milano

Argomenti del programma: • Introduction to software tools for data mining and statistical learning (e.g. R, Python) • Pre-processing and data cleaning: collinearity, dimensionality reduction (the filter strategy, the wrapper strategy, the embedded strategy), missing values, outliers. • Supervised approaches: binary and multiclass classification, generalized linear models for classification (e.g.

Argomenti del programma: • Introduction to software tools for data mining and statistical learning (e.g. R, Python) • Pre-processing and data cleaning: collinearity, dimensionality reduction (the filter strategy, the wrapper strategy, the embedded strategy), missing values, outliers. • Supervised approaches: binary and multiclass classification, generalized linear models for classification (e.g.