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.

Students should have completed at least one introductory course in statistics. A background in linear regression is beneficial but not mandatory. The course maintains a modest mathematical level, and a detailed understanding of matrix operations is not required. While prior experience with a programming language such as MATLAB or Python is helpful, it is not a prerequisite.

1. Introduction to Statistical learning and R programming.
2. Supervised learning: regression and classification.
3. Unsupervised learning: clustering and dimensionality reduction.

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, provided the written test has been successfully passed. The written test comprises multiple-choice questions. Examples of multiple-choice questions 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 are encouraged to register for the course on the Moodle platform (moodle.unive.it), where they can find supplementary materials.