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 | Code 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 | Code B |
| Lab 2 | Code Lab 2 | Code Lab 2 | |
| Exercises | Exercises B - Conceptual | Exercises B - Applied | |
| Linear Regression | Unit C | Slides Unit C | Code C |
| Lab 3 | Code Lab 3 | Code Lab 3 | |
| Exercises | Exercises C - Conceptual | Exercises C - Applied | |
| Tree-Based Methods | Unit D | Slides Unit D | Code D |
| Lab 4 | Code Lab 4 | Code Lab 4 | |
| Exercises | Exercises D - Conceptual | Exercises D - Applied | |
| PCA and Matrix Completion | Unit E | Slides Unit E | Code E |
| Lab 5 | Code 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 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.
Rules
Mock exam
Office hours
To schedule a meeting, please write to aldo.solari@unive.it. Office hours is every DAY at HOUR.
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.