Machine Learning (ECO-AD-MCHLEARN)
ECO-AD-MCHLEARN
| Department |
ECO |
| Course category |
ECO Advanced courses |
| Course type |
Course |
| Academic year |
2026-2027 |
| Term |
BLOCK 1 |
| Credits |
.5 (EUI Economics Department) |
| Professors |
- Prof. Anna Gottard
- University of Florence
|
| Contact |
Aleksic, Ognjen
|
| Sessions |
15/09/2026 11:00-13:00 @ Seminar Room 3rd Floor,V. la Fonte
22/09/2026 11:00-13:00 @ Conference Room, Villa la Fonte
24/09/2026 15:00-17:00 @ Conference Room, Villa la Fonte
29/09/2026 11:00-13:00 @ Seminar Room 3rd Floor,V. la Fonte
01/10/2026 15:00-17:00 @ Seminar Room 3rd Floor,V. la Fonte
|
| Enrolment info |
27/08/2026 - 15/09/2026 |
Description
Course Description:
This course introduces core methods and algorithms in machine learning from a statistical perspective. Beginning with the key concepts of supervised statistical learning, the course then covers shrinkage estimators for regression, including ridge regression, the lasso,
and their variants. In addition, it introduces and discusses essential tree-based algorithms for both regression and classification.
Text: James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning with Applications in R. Second edition, Springer International Publishing.
Syllabus
• Introduction to Statistical Learning:
¿ Expected risk in regression and classification
¿ Variance-bias trade-off
¿ Training set, Validation set, Test set, Cross-validation
• Shrinkage estimators for high dimensional regression and classification:
¿ Ridge
¿ Lasso
¿ Elastic net & Adaptive lasso
• Tree-based algorithms:
¿ CART
¿ Bagging and Random Forests
¿ Elements of boosting & BART
ENROL FOR THIS COURSE
Page last updated on 05 September 2023