Home » Departments and Centres » Academic catalogue » Course detail

Open sidebar menu

Machine Learning (ECO-AD-MACHLEARN)

ECO-AD-MACHLEARN

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