Machine Learning for Statistical
Genomics with Bioconductor
14:00 - 17:00
This lab reviews
basic concepts of supervised statistical learning
methods, including outcome and feature representation,
distance measures, families of learning procedures and
their tuning parameters, doubt and outlier decisions,
generalization error bounds and estimation.
Applications of machine learning procedures to
microarray data are presented using the Bioconductor
MLInterfaces package. Applications will involve
mechanical prediction tasks and substantive
interpretation via feature importance measurement.