CS2 – Risk Modelling and Survival Analysis
5个单元 · 21个章节
免费的CS2 – Risk Modelling and Survival Analysis学习笔记，专为IFoA (Institute and Faculty of Actuaries)学生准备。下列每个章节都涵盖一个重点主题，附有例题与练习提示，可在 thinka 应用中延伸练习。
Random variables and distributions for risk modelling
Time series
Stochastic processes
Survival models
Machine learning
Bias/variance trade-off and relationships with model complexity
Cross-validation to evaluate models on unseen data, and estimate hyper-parameters
Understand how regularisation can be used to reduce overfitting in highly parameterised models
The use of software to apply supervised learning techniques, to solve regression and classification problems
The use of metrics such as precision, recall, F1 score and diagnostics such as the ROC curve and confusion matrix to evaluate the performance of a binary classifier
Unsupervised learning techniques (principal component analysis, K-means clustering) to reduce data dimensionality, identify latent substructure and detect anomalies
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