Machine Learning (ECO-AD-MACHLEARN)
ECO-AD-MACHLEARN
Department |
ECO |
Course category |
ECO Advanced courses |
Course type |
Course |
Academic year |
2024-2025 |
Term |
BLOCK 4 |
Credits |
.5 (EUI Economics Department) |
Professors |
- Prof. Anna Gottard
- University of Florence
|
Contact |
Aleksic, Ognjen
|
Sessions |
02/05/2025 11:00-13:00 @ Seminar Room 3rd Floor,V. la Fonte
05/05/2025 16:15-18:15 @ Seminar Room 3rd Floor,V. la Fonte
08/05/2025 16:15-18:15 @ Seminar Room 3rd Floor,V. la Fonte
09/05/2025 11:00-13:00 @ Seminar Room 3rd Floor,V. la Fonte
12/05/2025 16:15-18:15 @ Seminar Room 3rd Floor,V. la Fonte
|
Enrollment info |
Contact ognjen.aleksic@eui.eu for enrolment details. |
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
Register for this course
Page last updated on 05 September 2023