Machine Learning in Finance

Crédit : 3 ECTS
Langue du cours : anglais

Volume horaire

• CM : 21 h

Compétences à acquérir

To understand the principles of supervised and unsupervised learning. Some Statistical Learning results are presented and applied to credit rating, anomalies detection and yield curves modelling. The principal notions are presented in the context of these case studies in finance.

Description du contenu de l'enseignement

· Introduction to statistical learning: The Vapnik Chervonenkis dimension, PAC learning and the calibration versus prediction paradigm. · Primal and Dual Problem, Lagrangian and KKT conditions · Supervised learning: SVM, Mercer’s theorem and the kernel trick, C-SVMs, mu-SVMs, a few words on SVMs for regressions. · Unsupervised learning: Single class SVMs, clustering, anomaly detection, equivalence of different approaches via duality. · Introduction to random forests and ensemble methods: bias variance trade-off, bootstrap method · Remarks on parsimony and penalisation: Ridge and Lasso regressions, dual interpretation of Lasso.

Mode de contrôle des connaissances

Exam

Pré-requis recommandés

Basic linear algebra and differential calculus.

Pré-requis obligatoires

Basic linear algebra and differential calculus.

Bibliographie, lectures recommandées

[1] James, Hastie, Witten, Tibshirani, Taylor; An introduction to Statistical Learning: file: https://hastie.su.domains/ISLP/ISLP_website.pdf.download.html [2] A Burkov; The hundred-pages machine learning book : chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/http://ema.cri-info.cm/wp-content/uploads/2019/07/2019BurkovTheHundred-pageMachineLearning.pdf

Enseignant responsable

PIERRE BRUGIERE