INTRODUCTION TO STATISTICAL LEARNING

This course introduces statistical methods for data analysis and prediction, which are categorized into supervised and unsupervised learning. Supervised statistical learning involves the development of a model to predict or estimate an output based on one or more input variables. This approach is widely applicable across various domains, including business, medicine, astrophysics, and public policy. In contrast, unsupervised statistical learning deals with data that consists solely of input variables, without a predefined output, enabling the identification of underlying patterns and relationships within the data.

The course aims to introduce students to statistical learning methods through practical applications in marketing, finance, biology, and other fields. The objective is to equip students with the skills to effectively analyze data by implementing statistical learning methods using the statistical software R.

Although the course draws on algebra, mathematics, probability, statistics, and programming, it is introductory and accessible to students from diverse backgrounds. A basic mathematical foundation is sufficient; no advanced mathematics is required. An elementary knowledge of statistics (e.g., Introduction to Probability for Economics) is recommended but not required. Familiarity with linear regression and prior exposure to a programming language such as R or Python are helpful but not necessary. No detailed knowledge of matrix operations is expected.

1. Introduction to Statistical learning and R programming.
2. Supervised learning: the bias-variance trade-off, linear regression, tree-based methods.
3. Unsupervised learning: dimensionality reduction, principal components analysis and matrix completion.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in R. 2nd ed. New York: Springer.

The final assessment consists of a written test, followed by an oral interview, conditional on successful completion of the written test. The written test is open book and requires students to bring their own laptop. It consists of the submission of a compiled R Markdown PDF file presenting a complete data analysis. Examples of written tests are available on the Ca’ Foscari Moodle e-learning platform.

written and oral

The exam is graded on a scale from 0 to 30, with a minimum passing score of 18. To achieve a score of 27 or higher, students must demonstrate effective data analysis using R programming during the oral interview. Honors ("lode") will be granted only for exceptional capacity of judgment and excellent knowledge of the topics under evaluation.

The course will be delivered in a lecture-style format, with select sessions dedicated to programming. Students must bring their own laptop to these sessions and have R and RStudio installed.

Students are encouraged to register for the course on the Moodle platform (moodle.unive.it), where they can find supplementary materials.