ACTSC 856Statistical Learning in Actuarial Science

This course explores a broad range of statistical learning models used to analyze data in actuarial and financial contexts. It covers techniques from both supervised and unsupervised learning, with a focus on practical applications. Specific topics covered include: modeling principles and practice, advanced and regularized regression models, cross-validation, traditional classification models (logistic regression, LDA, QDA, KNN), decision trees (bagging, boosting, random forests), dimensionality reduction (PCA), hierarchical and K-means clustering, and an introduction to neural networks with applications to supervised and unsupervised learning problems. Some extensions to the above models may also be considered.