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CS2 – Risk Modelling and Survival Analysis
5 sections available · 21 chapters available
Free CS2 – Risk Modelling and Survival Analysis study notes for IFoA (Institute and Faculty of Actuaries) students. Each chapter breaks down a key concept with examples and practice prompts you can turn into AI drills in the thinka app.
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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