Insurance analytics
Data is displayed for the academic year: 2024./2025.
Lecturers
Course Description
Technology development has enabled the collection of large data sets at high speed, which opens up new challenges and opportunities for applications in various fields, including insurance. This course focuses on the problem of knowledge discovery from structured and unstructured insurance data, including large datasets (big data). The course gives a comprehensive introduction into the procedures and quantitative methods for knowledge discovery from data and connects two integral aspects vital to data-driven decision making: applied statistics and machine learning. Visualization and exploratory data analysis procedures are introduced, together with the basic principles of statistical inference. Classification, regression, clustering and dimensionality reduction problems are considered. A special part of the course is directed at a selection of machine learning models, with an introduction to deep learning. In addition to familiarizing with modern knowledge discovery concepts, attention is given to the application of this knowledge through working with real world data. The course will combine lectures, guest lecturers, case study analyses and team work.
Study Programmes
Postgraduate spec. study
Literature
General
ID 209125
Summer semester
5 ECTS
L3 English Level