The course aims to explore the fundamental concepts of statistical learning, introducing a wide range of tools and techniques such as linear and nonlinear models, decision trees, neural networks, and dimensionality reduction methods. The main objective is to provide both theoretical knowledge and practical skills in data manipulation and analysis, as well as in the evaluation and interpretation of model results with particular attention to supervised learning problems. Real-world problems will be addressed to understand the theoretical principles underlying learning algorithms and to learn their effective application using specific libraries of the statistical software R.
Curriculum
scheda docente
materiale didattico
• Prediction and classification problems;
• Comparison of classification methods;
• Resampling methods: cross-validation and bootstrap;
• Dimensionality reduction methods: ridge regression and lasso;
• Nonlinear methods: spline regression;
• Methods based on decision trees: regression trees, classification trees, bagging, random forests, boosting;
• Use of the statistical environment R.
Course materials will be available on the Teams class of the course.
Programma
• Introduction to major statistical learning models;• Prediction and classification problems;
• Comparison of classification methods;
• Resampling methods: cross-validation and bootstrap;
• Dimensionality reduction methods: ridge regression and lasso;
• Nonlinear methods: spline regression;
• Methods based on decision trees: regression trees, classification trees, bagging, random forests, boosting;
• Use of the statistical environment R.
Testi Adottati
G. James, D. Witten, T. Hastie, R. Tibshirani (2020) Introduction to Statistical Learning, Piccin Publishing House.Course materials will be available on the Teams class of the course.
Modalità Valutazione
Oral examination.
scheda docente
materiale didattico
• Prediction and classification problems;
• Comparison of classification methods;
• Resampling methods: cross-validation and bootstrap;
• Dimensionality reduction methods: ridge regression and lasso;
• Nonlinear methods: spline regression;
• Methods based on decision trees: regression trees, classification trees, bagging, random forests, boosting;
• Use of the statistical environment R.
Course materials will be available on the Teams class of the course.
Mutuazione: 21210514 Statistical learning in Economia e Gestione della Trasformazione Digitale LM-56 R FORTUNA FRANCESCA
Programma
• Introduction to major statistical learning models;• Prediction and classification problems;
• Comparison of classification methods;
• Resampling methods: cross-validation and bootstrap;
• Dimensionality reduction methods: ridge regression and lasso;
• Nonlinear methods: spline regression;
• Methods based on decision trees: regression trees, classification trees, bagging, random forests, boosting;
• Use of the statistical environment R.
Testi Adottati
G. James, D. Witten, T. Hastie, R. Tibshirani (2020) Introduction to Statistical Learning, Piccin Publishing House.Course materials will be available on the Teams class of the course.
Modalità Valutazione
Oral examination.