Università Roma Tre

The course will cover the following topics:
- Introduction to the main models of statistical learning;
- Prediction and classification problems: Recalls on linear regression and the main methods of unsupervised classification;
- Supervised classification: K-Nearest-Neighbours;
- Misclassification error;
- Resampling methods: cross validation and bootstrap;
- Decision tree-based methods: regression trees, classification trees, bagging, random forests, boosting.
- Introduction to semi-supervised classification methods;
- Use of the statistical environment R

James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R (2nd ed.). Springer.