MIDAS Seminar

PCS Framework, Interpretable Machine Learning, and Deep Neural Networks

Bin Yu, PHDChancellor's ProfessorElectrical Engineering and Computer Science University of California, Berkeley
Weiser HallMap

In this talk, Dr. Yu will discuss the intertwining importance and connections of three principles of data science: predictability (PCS), computability and stability (workflow and documentation). She will also define interpretable machine learning through the PDR desiderata (Predictive accuracy, Descriptive accuracy and Relevancy) and discuss stability as a minimum requirement for interpretability. PCS and PDR will be demonstrated in the context of one collaborative project in neuroscience, DeepTune, for interpretable data results and testable hypothesis generation.