ECONOMIA E IMPRESAData ScienceAcademic Year 2022/2023

9793875 - DATA ANALYSIS AND STATISTICAL LEARNING
Module STATISTICAL LEARNING

Teacher: Salvatore INGRASSIA

Expected Learning Outcomes

The module provides knowledge about: i) the statistical learning problem and the general model of learning from empirical data; ii) main statistical learning techniques for regression and data classification.

Course Structure

Lectures and practical data modeling in R.

Required Prerequisites

Knowledge of algebra, mathematical analysis, geometry, probability (at bachelor level).

Attendance of Lessons

In person.

Detailed Course Content

Statistical Learning. Estimation of dependences based on empirical data. Supervised and Unsupervised Learning. Regression and Classification problems. Parametric and non-parametric models. Assessing Model Accuracy.

Linear Regression. Simple linear regression. Multiple linear regression. Least squares criterion and parameter estimation. Assessing the accuracy of the coefficient estimates and of the model. Use of qualitative predictors. Extension of the linear model and non-linear relationships.

Classification. Logistic regression; parameter estimation. Linear and quadratic discriminant analysis.

Resampling methods. Cross-validation, Bootstrap.

Tree-based Methods. Regression Trees and Classification Trees. Bagging, Random Forest, Boosting

Deep learning. Single layer networks and multilayer networks. Fitting a neural network.

Textbook Information

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

2. Hastie T., Tibshirani R., Friedman (2008). The Elements of Statistical Learning, Springer, New York

3. Course notes

Author	Title	Publisher	Year	ISBN
James G., Witten D., Hastie T., Tibshirani R.	An Introduction to Statistical Learning with Applications in R	Springer	2021
Hastie T., Tibshirani R., Friedman	The Elements of Statistical Learning	Springer	2008

Course Planning

	Subjects	Text References
1	Basics of statistical learning.	Textbook n.1, chap. 1
2	Linear Regression	Textbook n.1, chap. 1
3	Classification	Textbook n.1, par. 4.1-4.5
4	Resampling methods	Textbook n.1, chap. 5
5	Tree-based methods	Textbook n.1, chap. 8
6	Deep learning	Textbook n.1, par. 10.1, 10.2, 10.6, 10.7

Learning Assessment

Learning Assessment Procedures

The evaluation will be based on a data analysis report provided by students and oral exam.

Examples of frequently asked questions and / or exercises

see the course content.

Versione in italiano

9793875 - DATA ANALYSIS AND STATISTICAL LEARNING Module STATISTICAL LEARNING