Introduction to Applied Machine Learning for Threat Intelligence Analysis

Course Description

Students will be introduced to applied machine learning (ML) to support cyber and other threat intelligence investigations and analysis. The course covers fundamental machine learning concepts, approaches, and best practices, including topics on classification, clustering, and model building and evaluation. These will be applied using Python through substantive examples within the realm of intelligence investigations and analysis to help students become familiar with how such approaches might be put to practice. The course will not be heavily focused on theory or the underlying math of models, but instead focus on developing student familiarity with basic applications of common ML approaches—geared towards students who have no, or very little, prior exposure to coding. students will be expected to do substantial work to develop their Python skills. Python and introduction to ML are typically taught as two separate classes, so tackling them both in a single course will be an ambitious undertaking for you, but a rewarding one. (Course is not for students with significant Python experience, as it will be too slow paced—contact professor if you have questions.)

Code

SA.502.191

Credits

4

Subject

Security, Strategy and Statecraft

Programs That Include This Course

• Master's Strategy, Cybersecurity, and Intelligence, Master of Arts