Overview

Digital forensics is a rapidly evolving field, and staying up-to-date with the latest technology is essential for success. This Machine Learning for Digital Forensics short course is designed to provide attendees with a comprehensive overview of machine learning and how it can be applied to Digital Forensic Investigations. 

Attendees will learn the differences in supervised and unsupervised machine learning models and the algorithms that support them. The course will compare and discuss use cases for a range of learning algorithms including Regression Algorithms, Decision Trees, Naïve Bayes, SVM (Support Vector Machines) and KNN (K-Nearest Neighbors). Through hands-on practical examples attendees will learn how to apply these algorithms to leverage the power of machine learning for digital forensic tasks. 

The course will also consider the potential value of ChatGPT for digital forensic investigations and attendees will learn about the limitations of machine learning algorithms and methods for assessing their performance and accuracy. By the end of the course, attendees will have a solid understanding of the fundamentals of machine learning and how it can be used to improve digital investigation workflow efficiency without compromising on accuracy of results.