Duration: 2 days

Time: 0930-1700

Venue: Copthorne King's Hotel

Synopsis: 

This course introduces the foundational principles and practical applications of Machine Learning for Electronic Warfare, designed for learners with no prior background in either field. Through theoretical grounding and hands-on experience, participants will explore how ML can address key challenges in EW and drive the next generation of decision-making in the electromagnetic battle space. Through a combination of theoretical foundations and hands-on exercises, the course demystifies key concepts in supervised and unsupervised learning, deep learning, reinforcement learning and signal processing. Examples and case studies from defence and communications scenarios provide practical context, enabling participants to bridge the gap between data-driven methods and mission-critical applications.

Target Audience:

This course is intended for EW professionals who seek to apply ML techniques within the EW domain, as well as for AI and machine learning practitioners aiming to extend their expertise to applications in electronic warfare. This course is designed for professionals from diverse backgrounds, with no prior experience required in either machine learning or electronic warfare. It serves both those seeking to understand how ML can be applied to EW scenarios and those interested in entering the EW domain through the lens of modern AI techniques. This course aims to equip the participants with the knowledge and tools to begin leveraging machine learning in the context of electronic warfare.

Contents:

1. Introduction to EW and ML

2. RF Signal Processing and Data Collection

3. Supervised Learning for EW Applications

4. Deep Learning and Neural Networks in EW

5. Unsupervised Learning and Anomaly Detection

6. Reinforcement Learning for EW Strategies 

7. Ethical and Security Considerations in ML for EW