Journal article
Tutorial overview of simple, stratified, and parametric bootstrapping
Engineering reports (Hoboken, N.J.), v 2(1), pn/a
01 Jan 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
Metrics
Details
- Title
- Tutorial overview of simple, stratified, and parametric bootstrapping
- Creators
- P. M. Shankar - Drexel University
- Publication Details
- Engineering reports (Hoboken, N.J.), v 2(1), pn/a
- Publisher
- Wiley
- Number of pages
- 10
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000674330600013
- Scopus ID
- 2-s2.0-85086254641
- Other Identifier
- 991019184076104721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Multidisciplinary
- Materials Science, Multidisciplinary