This course will introduce the foundational concepts in predictive modeling with emphasis on blending interpretability and generalizability which are important in health sciences applications. The classical linear models, such as ordinary least squares and logistic regressions will be recast as supervised learning tools for regression and classification tasks, respectively, and then extended through common techniques of basis expansion and regularization. A second part of the course will focus on many aspects of estimating prediction performance of any method. The course will blend theoretical framework and simulation-based computational approaches to enhance the understanding of issues such as bias-variance trade-off, overfitting, generalization performance to prepare students to critically appraise the use of, and effectively deploy simple and more complex supervised learning techniques. The course will also serve as a preparation for studying more complex methods (such as in CHL5229H, for which this course is meant to be a prerequisite).