Critical Readings in Biomedical Statistics and Machine Learning

A survey of recurring statistical errors and pitfalls which are sometimes used to exaggerate the weight of evidence for novel biological claims or inflate the estimated accuracy of proposed predictive biomedical models. This course focuses on misapplied analyses of data sources where a small number of biological samples are quantified into very high dimensional feature spaces, such as in genomics, proteomics, and biomedical imaging.