Tutorial overview of simple, stratified, and parametric bootstrapping

Abstract

Students pursuing baccalaureate degrees in electrical engineering and computer engineering are required to take a course in probability and statistics. While the course continues to be mostly conceptual, author started initiatives to introduce data analytics in this course with special emphasis on machine vision applications. Topics such as receiver operating characteristics curves and hypothesis testing are covered through examples and exercises with students having individual datasets. Continuing with this theme, bootstrapping and associated methodologies have now been introduced to facilitate interpretation of machine vision experiments. A demo created that illustrates simple, stratified, and parametric bootstrapping as a means to understand the statistics of a machine vision sensor is presented. It encompasses a number of conceptual topics such as random variables, densities, parameter estimation, chi square testing, etc. alongside data analytics offering a holistic picture of machine learning and machine vision to the undergraduate students.