Campus Units

Statistics, Center for Statistics and Applications in Forensic Evidence (CSAFE)"

Document Type

Presentation

Conference

Proceedings of the 2020 American Academy of Forensic Sciences

Publication Version

Published Version

Publication Date

2-17-2020

Conference Title

Proceedings of 2020 American Academy of Forensic Sciences

Conference Date

February 17- 22, 2020

City

Anaheim, CA

Abstract

Learning Overview: The goals of this workshop are to: (1) introduce attendees to the basics of supervised learning algorithms in the context of forensic applications, including firearms and footwear examination and trace evidence, while placing emphasis on classification trees, random forests, and, time permitting, neural networks; (2) introduce the concept of a similarity score to quantify the similarity between two items; (3) show how learning algorithms can be trained to classify objects into pre-determined classes; (4) discuss limitations of Machine Learning (ML) algorithms and introduce methods for assessing their performance; and (5) discuss the concept of a Score-based Likelihood Ratio (SLR): computation, advantages, and limitations.

Comments

Carriquiry, A.L., Hofmann, H., Salyards, M.J., Thompson, R.M., Statistical Learning Algorithms for Forensic Scientists. AAFS 2020 Anaheim, CA. Posted with permission from CSAFE.

Copyright Owner

The Author(s)

Language

en

File Format

application/pdf

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