Introduction to Statistical Learning
Bachelor’s Degree Programme in Philosophy, International and Economic Studies, Ca’ Foscari University of Venice.
This is the website of the Introduction to Statistical Learning course of the Bachelor’s Degree Programme in Philosophy, International and Economic Studies, Ca’ Foscari University of Venice.
Teaching material
Required
- Gareth, J., D. Witten, T. Hastie, and R. Tibshirani. 2021. An Introduction to Statistical Learning with Applications in R (Second Edition). New York: Springer.
Optional
Azzalini, A., and B. Scarpa. 2013. Data Analysis and Data Mining: an Introduction. Oxford University Press.
Gareth, J., D. Witten, T. Hastie, R. Tibshirani and J. Taylor. 2023. An Introduction to Statistical Learning with Applications in Python. New York: Springer.
Slides and lecture notes
The slides are meant to be used in HTML. However, if you really want to convert the HTML slides into pdf files, you can follow the instruction of the quarto documentation.
| Topic | Notes | Slides & Code |
|---|---|---|
| Introduction | Unit A | Slides Unit A |
| Lab 1 | Lab 1 | Code Lab 1 |
| Exercises | Exercises A - Conceptual | Exercises A - Applied |
| — | — | — |
| The Bias-Variance Trade-Off | Unit B | Slides Unit B |
| Lab 2 | Lab 2 | Code Lab 2 |
| Exercises | Exercises B - Conceptual | Exercises B - Applied |
| — | — | — |
| Linear Regression | Unit C | Slides Unit C |
| Lab 3 | Lab 3 | Code Lab 3 |
| Exercises | Exercises C - Conceptual | Exercises C - Applied |
| — | — | — |
| Tree-Based Methods | Unit D | Slides Unit D |
| Lab 4 | Lab 4 | Code Lab 4 |
| Exercises | Exercises D - Conceptual | Exercises D - Applied |
| — | — | — |
| PCA and Matrix Completion | Unit E | Slides Unit E |
| Lab 5 | Lab 5 | Code Lab 5 |
| Exercises | Exercises E - Conceptual | Exercises E - Applied |
Prerequisites
Basic mathematical background (no advanced mathematics needed)
Elementary knowledge of statistics is strongly recommended but not required (e.g., Introduction to Probability for Economics)
Familiarity with linear regression is helpful but not required
No detailed knowledge of matrix operations is required
Previous exposure to a programming language (e.g., R or Python) is useful but not required
Labs
The course is delivered in a lecture format, with selected sessions devoted to programming activities (``labs’’). Students must bring their own laptop to these sessions and have R and RStudio installed.
Exam
The exam consists of two parts:
Data analysis project, which aims to evaluate your ability to build predictive models. You must submit:
- your predictions, together with the code used to reproduce them;
- a report (maximum 4 pages) describing your analysis.
The data analysis must be uploaded on the course Moodle page at least three days before the exam date and can be submitted only once per academic year.
- your predictions, together with the code used to reproduce them;
Oral exam, which may include a discussion of your data analysis and questions on the topics covered in class, including assigned exercises and programming.
Office hours
Office hours are held on Fridays at 11:00 AM in office C2.111, San Giobbe Economic Campus. Please book an appointment in advance by email.
Acknowledgments
The primary source of this website’s content is the textbook Gareth, Witten, Hastie and Tibshirani (2023). A few pictures have been taken from the textbook; in these cases the original source is cited.
I am also grateful to Tommaso Rigon for the use of his Quarto template.
All the mistakes still present are mine.