Statistical learning 2
Ects : 4
Enseignant responsable :
Volume horaire : 39Description du contenu de l'enseignement :
We will cover the following topics in this course:
- Least squares regression
- Ridge regression
- LASSO
- PCA
- Kernel methods
Pré-requis obligatoires :
- Linear algebra
- Basic probability
Compétence à acquérir :
The goal of this course is to get acquainted with the mathematics behind the classical machine learning algorithms.
Mode de contrôle des connaissances :
- 25% Midterm exam
- 75% Final exam
Bibliographie, lectures recommandées
The material in this course takes inspiration from the following excellent ressources:
- Bach, Francis. Learning theory from first principles. MIT press, 2024.
- Hastie, Trevor, et al. The elements of statistical learning: data mining, inference, and prediction. Vol. 2. New York: springer, 2009.
- Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
- Wasserman, Larry. All of statistics: a concise course in statistical inference. Springer Science & Business Media, 2013.