Machine Learning in Finance

Ects : 3
Compétence à acquérir :
Vapnik Chervonenkis dimension, PAC learning, calibration versus prediction, SVM (Support Vector Machines) classifiers, Mercer's theorem, C-SVMs, mu-SVMs and single class SVMs. Basics of decision trees, random forests and penalized regressions.

Description du contenu de l'enseignement :
Methods of Statistical Learning, applied to some financial problems of credit rating, anomaly detection and yield curve approximations