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In this module, we will present a number of supervised learning techniques for the analysis of Biomedical Big Data. These techniques include penalized approaches for performing regression, classification, and survival analysis with Big Data. Support vector machines, decision trees, and random forests will also be covered.

The main emphasis will be on the analysis of “high-dimensional” data sets from genomics, transcriptomics, metabolomics, proteomics, and other fields. These data are typically characterized by a huge number of molecular measurements (such as genes) and a relatively small number of samples (such as patients). We will also consider electronic health record data sets, which often contain many missing measurements.

Throughout the course, we will focus on common pitfalls in the supervised analysis of Biomedical Big Data and how to avoid them. The techniques discussed will be demonstrated in R.

This course assumes some previous exposure to linear regression and statistical hypothesis testing, as well as some familiarity with R or another programming language (see previous years materials for reference).

Recommended Reading: James et al. (2013) Introduction to Statistical Learning. Springer Series in Statistics. Available for free download.