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Insurance Analytics

  • Enseignant(s):   P.Hieber  
  • Titre en français: Analyse de données en assurance
  • Cours donné en: anglais
  • Crédits ECTS: 3 crédits
  • Horaire: Semestre d'automne 2021-2022, 2.0h. de cours (moyenne hebdomadaire)
  •  séances
  • site web du cours site web du cours
  • Formation concernée: Maîtrise universitaire ès Sciences en sciences actuarielles
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Objectifs

The goal is to provide a solid understanding of some of the most common machine learning methods, and to be able to apply and interpret them on insurance data.

Contenus

  • Model Performance Evaluation (Model selection, Model Asessment)
  • Regression Trees
  • Bagging Trees and Random Forests
  • Boosting Trees and Gradient Boosting Trees
  • Measures for Model Comparison (Measures of Association, Tools to Measure Model Lift)
  • Introduction to Neural Networks

Références

Denuit, M., Hainaut, D., Trufin, J. (2020). Effective Statistical Learning Methods for Actuaries II. Tree-Based Methods. Lecture Notes.

Denuit, M., Hainaut, D., Trufin, J. (2019). Effective Statistical Learning Methods for Actuaries III. Neural Networks and Extensions. Springer Actuarial Lecture Notes.

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Second Edition. Springer Series in Statistics.

Kuhn, M., Johnson, K. (2013). Applied Predictive Modeling. Springer, New York.

Wüthrich, M. V., Buser, C. (2019). Data analytics for non-life insurance pricing. Lecture notes.

Evaluation

1ère tentative

Examen:
Ecrit 1h30 heures
Documentation:
Non autorisée
Calculatrice:
Autorisée
Evaluation:

Grades are based on a final exam.

No documentation allowed.

Due to health developments linked to COVID-19, the study plans (and evaluation criteria) may experience adaptations during the semester.

Rattrapage

Examen:
Ecrit 1h30 heures
Documentation:
Non autorisée
Calculatrice:
Autorisée
Evaluation:

Grades are based on a final exam.

No documentation allowed.

Due to health developments linked to COVID-19, the study plans (and evaluation criteria) may experience adaptations during the semester.



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