This course covers the use of computational methods in crime investigation (forensics) and prevention (intelligence). In almost all areas of forensics and intelligence, computational methods continue to aid, and sometimes entirely replace, human expertise in tracking crime. This is desirable since automation can address the problems associated with scale and global crime linkage through diverse data computational tools can potentially overcome and surpass human capabilities for crime investigation. This course is of a cross-disciplinary nature. It amalgamates knowledge from criminology, forensic sciences, computer science, statistics, signal processing, machine learning, AI, psychology, medicine and many other fields.

Syllabus

https://forensics-ai.github.io/compfor21/

Class format

Lecture and project-based

Home department

Language Technologies Institute

Target audience

Students from all departments and schools are welcome to take this course.

Background required

You must know programming (preferably Python). Basic skills in maths, statistics and probability are expected.

Learning objectives

By the end of the course, students will learn some of the key technologies that are being used to track cybercriminals. Students gain a broad understanding of the computational methods used in cyberforensics and multimedia forensics, with focus on
AI-based methods. The course will give students the knowledge and confidence required to formulate innovative multi-disciplinary solutions to complex forensic
problems.

Faculty and instructors who have taught this course in the past

Rita Singh