Scheda insegnamento


L'insegnamento contribuisce al perseguimento degli Obiettivi di Sviluppo Sostenibile dell'Agenda 2030 dell'ONU.

Istruzione di qualità

Anno Accademico 2020/2021

Conoscenze e abilità da conseguire

By the end of the course the student knows the fundamentals of the most important multivariate techniques to build supervised statistical models for predicting or estimating an output based on one or more inputs. The student is able to represent and organize knowledge about large-scale data collections, and to turn data into actionable knowledge.


Part 0: Introduction to Supervised Statistical Learning

Part 1: Resampling methods

  • Cross-Validation

Part 2: Classification

  • Naive Bayes
  • k-Nearest Neighbours
  • Logistic Regression
  • Linear Discriminant Analysis

Part 3: Dimension Reduction and Regularisation

Part 4: Tree-based methods

  • Regression and Classification trees
  • Bagging; Random Forests; Boosting

Part 5: Overview of the main machine learning methods

  • Support Vector Machines
  • Neural Networks


The primary text for the course:

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to Statistical Learning. New York: Springer.
    Freely available at: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf

In addition, we will use:

  • T. Hastie, R. Tibshirani, and J. Friedman (2001) The Elements of Statistical Learning: data mining, inference and prediction. Springer Verlag.
    Freely available at: https://web.stanford.edu/~hastie/Papers/ESLII.pdf

Metodi didattici

Lectures and practical sessions.

Modalità di verifica dell'apprendimento

Written exam with theoretical questions and practical exercises to be solved in R.

Strumenti a supporto della didattica

The following material will be provided: slides of the lectures, exercises with solutions, mock exam.

Orario di ricevimento

Consulta il sito web di Laura Anderlucci