Stanford Seminar - ML Explainability Part 2 I Inherently Interpretable Models

Professor Hima Lakkaraju presents some of the latest advancements in machine learning models that are inherently interpretable such as rule-based models, risk scores, generalized additive models and prototype based models. View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPh6wa6PGcHH6vMG9sEIPxL #machinelearning 0:00 Introduction 0:06 Inherently Interpretable Models 4:41 Bayesian Rule Lists: Generative Model 8:39 Pre-mined Antecedents 11:09 Interpretable Decision Sets: Desiderata 12:16 IDS: Objective Function 15:47 IDS: Optimization Procedure 16:46 Risk Scores: Examples 18:59 Objective function to learn risk scores 21:54 Generalized Additive Models (GAMs) 23:00 Formulation and Characteristics of GAMS 30:04 Prototype Selection for Interpretable Classification 34:00 Prototype Layers in Deep Learning Models 40:01 Attention Layers in Deep Learning Models