Safer and More Interpretable Machine Learning Models For Healthcare

Dr. Feng will discuss a range of recent research topics on making machine learning (ML) algorithms safer and more interpretable for medical decision making. This includes a new variable importance measure for black-box models, prediction algorithms with the option to abstain, and policies that regulate when and how often ML algorithms can be updated. 

Speaker: Jean Feng, PhD, Assistant Professor In Residence, Department of Epidemiology and Biostatistics, UCSF 

REGISTER LINK: http://eepurl.com/g1X35P