401-3936-00L Data Analytics for Non-Life Insurance Pricing
| Semester | Frühjahrssemester 2023 |
| Dozierende | M. V. Wüthrich, C. M. Buser |
| Periodizität | jährlich wiederkehrende Veranstaltung |
| Lehrsprache | Englisch |
| Kurzbeschreibung | We study statistical methods in supervised learning for non-life insurance pricing such as generalized linear models, generalized additive models, Bayesian models, neural networks, classification and regression trees, random forests and gradient boosting machines. |
| Lernziel | The student is familiar with classical actuarial pricing methods as well as with modern machine learning methods for insurance pricing and prediction. |
| Inhalt | We present the following chapters: - generalized linear models (GLMs) - generalized additive models (GAMs) - neural networks - credibility theory - classification and regression trees (CARTs) - bagging, random forests and boosting |
| Skript | The lecture notes are available from: M.V. Wüthrich, C. Buser. Data Analytics for Non-Life Insurance Pricing http://ssrn.com/abstract=2870308 |
| Literatur | M.V. Wüthrich, M. Merz. Statistical Foundations of Actuarial Learning and its Applications http://ssrn.com/abstract=3822407 |
| Voraussetzungen / Besonderes | The exams ONLY take place during the official ETH examination period (no semester end exams), and they will only be taken in person (no remote exams). This course will be held in English and counts towards the diploma of "Aktuar SAV". For the latter, see details under www.actuaries.ch Good knowledge in probability theory, stochastic processes and statistics is assumed. |