The course aims to introduce the fundamental principles of statistical learning for data analysis, with particular attention to regression and classification problems in supervised settings. By the end of the course, students will be able to understand the main statistical models for predictive analysis; evaluate and select models using statistical criteria and validation methods; interpret model results and assess their performance; use R (and RStudio) software to implement basic statistical learning analyses. The course emphasizes an applied approach, with practical examples based on real data.
teacher profile teaching materials
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
Programme
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
Core Documentation
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R (2nd ed.). Springer.Type of evaluation
Oral examination. During the course, two midterm exams will be administered.