We will introduce the core machine learning concepts required to experiment with survival methods. The chapter also introduces fundamental ideas in data splitting, resampling, cross-validation, benchmarking, and hyperparameter optimization, with an emphasis on estimating generalization error. 

By reading this article, we will know the conceptual framework and vocabulary required for supervised learning for survival analysis that will be used consistently throughout subsequent chapters. To know more, please check-out this article: https://www.mlsabook.com/P1C3_machinelearning.html#sec-ml-eval

This website covers a board range of concepts from performance evaluation to advanced risk minimization.  Multiple tuning strategies and nested re-sampling techniques are discussed in detail. 

The following topics are discussed here as well: 

1. Regularization

2. Boosting 

3. Gaussian Process

4. Imbalanced learning 

For more details, please check out: https://slds-lmu.github.io/i2ml/