Skip to content

Machine Learning & Statistical Algorithms Learning Path

Machine Learning & Statistical Algorithms Learning Path

A Learning Path is a set of resources organized around a theme within the CSAFE Learning environment. Below, we've outlined resources relevant to one of our core concepts: Machine Learning & Statistical Algorithms. You are not required to follow the topics or steps within a topic in sequential order. Instead, we encourage you to browse the library of resources and interact with those best suited to help you better understand statistics within the field.

Included in this Learning Path are the following learning opportunities:

Regression

⏱ 5 minutes

This beginner-level Stats Starter introduces regression analysis, explaining how to model the relationship between two variables and make predictions in forensic casework.

Stats Starters are easily digestible overviews that allow learners to explore a single foundational statistics concept and its application to forensic evidence. Each Stats Starter video includes an introduction and overview of the concept, statistical models and theories, and examples and applications.

Classification

⏱ 5 minutes

This beginner-level Stats Starter discusses the concept of classifying objects based on shared qualities or characteristics and how that applies to forensic evidence.

Stats Starters are easily digestible overviews that allow learners to explore a single foundational statistics concept and its application to forensic evidence. Each Stats Starter video includes an introduction and overview of the concept, statistical models and theories, and examples and applications.

Algorithms in Forensic Science

⏱ 30 minutes

This Foundational Learning opportunity presents key components of the use of algorithms in forensic science. The three sessions will address the following topics:

  • How the forensic science community is constantly called on for reform and how algorithms can play an important role in that process;
  • How algorithms can cohabitate with personal judgment when implemented at various levels; and
  • Barriers to adoption, including issues of validation, behavioral tendencies toward algorithms, and admissibility in court.

This learning opportunity is on-demand and available immediately.

Machine Learning for Forensic Practitioners

⏱ 6 hours

In this short course, Dr. Heike Hofmann provides an overview of machine learning and how it applies to forensic evidence. We introduce the basics of supervised learning algorithms in the context of forensic applications while emphasizing classification trees, random forests, and neural networks. Finally, Dr. Hofmann addresses some limitations of machine learning algorithms and how to introduce methods for assessing their performance.

This course should take learners approximately six hours to complete and is available immediately to learn at your own pace.

Price: $0.00