93050 - Supervised Statistical Learning

Course Unit Page

  • Teacher Laura Anderlucci

  • Credits 6

  • SSD SECS-S/01

  • Language English

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Statistical Sciences (cod. 9222)


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2021/2022

Learning outcomes

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.

Course contents

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. (2021). An introduction to Statistical Learning. Second Edition. New York: Springer. ISBN: 978-1-0716-1417-4. E-book ISBN 978-1-0716-1418-1

    Alternatively, the first edition (2013) is freely available here:


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

Teaching methods

Lectures and practical sessions.

Assessment methods

The learning assessment is composed by a written test lasting 70 minutes. The written test is aimed at assessing the student's ability to use the learned definitions, concepts and properties and in solving exercises. During the written exam, students can only use the cheat sheet that is provided on virtuale.unibo.it, containing references to R packages and functions. Students cannot make use of the textbook, personal notes and mobile phones (smart watch or similar electronic data storage or communication device are not allowed either).

The written test consists of 5-7 questions, both multiple choice and open, some of which to be solved in R. The final grade is out of thirty.

Students that, despite having passed the exam, do not feel represented by the obtained result can ask to have an additional (optional) oral exam that can change the grade by +/-3 points.

Teaching tools

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

Office hours

See the website of Laura Anderlucci