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Introduction to Machine Learning for Electronic Warfare

Introduction to Machine Learning for Electronic Warfare

September 14 - 28, 2020 | Mondays, Wednesdays & Fridays | 13:00-16:00 PM EDT (17:00 – 20:00 UTC)

Introduction to Machine Learning for Electronic Warfare - Live Web Course


When: September 14 - 28, 2020 | Mondays, Wednesdays & Fridays | 13:00-16:00 PM EDT (17:00 – 20:00 UTC)

Where: Live Web Course - No Travel Required

Course Length: 21 hours total - delivered across 7 sessions of 3-hours each.


  • PLEASE NOTE: This course will be delivered through Adobe Connect. Each session will be recorded and made available to all registrants for 30 days after the course. If you miss a session or two, you can catch up by viewing the recording! To ensure your computer system has access to Adobe Connect, please test your system HERE.

Description

This course introduces students to the fundamentals of machine learning and its application to modern Electronic Warfare (EW) and cyber solutions. Commencing with an overview of machine learning, and its recent evolution into deep learning, the course focuses on providing an education in how these algorithms work and training on how to apply them in an EW context. As an example, machine learning using neural networks will be discussed, followed by a demonstration on how to implement one to solve a classification problem using Electronic Intelligence (ELINT).

The course contains the following major topics: introduction to machine learning, classification using neural networks, training machine learning systems for EW, and developing solutions using machine learning for EW. Each topic will include lectures, demonstrations, and, for the more ambitious, an exercise to further explore the capabilities of machine learning in EW and cyber applications.

Who Should Attend

The intended audience of this course are Electronic Warfare (EW) professionals looking to expand their knowledge of the field and machine learning. No prior experience in EW is required, but a background in engineering or science is recommended.

Required Course Materials

None

  1. Session 1 - Introduction to Machine Learning and EW
    1. Course Introduction
    2. Machine Learning
    3. Electronic Attack Systems
    4. Electronic Support Systems
  2. Session 2 - Probability and Statistics
    1. Probability
    2. Random Variables and Processes
    3. Estimation and Decision Theory
  3. Session 3 - Machine Learning Concepts
    1. Regression
    2. Classification
    3. Neural Networks
  4. Session 4 - Software for Machine Learning
    1. Introduction to Python
    2. Numpy and its Applications
    3. Python Machine Learning Libraries
  5. Session 5 - Applying Machine Learning
    1. Machine Learning Tools
    2. Deep Learning
    3. Lab – Developing a Neural Network
  6. Session 6 - Machine Learning and ELINT
    1. Radar Waveforms
    2. Communications Waveforms
    3. Lab – Developing Training Data
  7. Session 7 - Machine Learning in Operation
    1. Unsupervised Learning
    2. Reinforcement Learning
    3. Classifying ELINT
    4. Lab – Unsupervised Learning and ELINT
    5. Conclusion

AOC Members - $1400

Non AOC Members - $1450

  • NOTE: Each registration is for one (1) participant ONLY. Distributing your registration URL or allowing others to participate in this course with you or under your account is grounds for removal from the course without refund of any kind.

Kyle Davidson is a former signals officer, having served for 15 years in the Canadian Army. During this time, he held a variety of positions in the field force, on operations in Afghanistan, and as an educator. For the last five years in the Army he served at the Royal Military College of Canada (RMC) as an assistant professor in the Department of Electrical and Computer Engineering, from which he holds a B.Eng. and M.A.Sc. He continues to serve as an adjunct professor at RMC and is scheduled to defend his Ph.D. in EW systems engineering in the spring of 2019. Since leaving the Canadian Armed Forces he has worked as a Radar and Electronic Warfare Scientist and later Head of Capability at Tactical Technologies Inc., a subsidiary of Leonardo MW, on a variety of projects, often related to the Eurofighter Typhoon's defensive aid suite. He is currently the Chief Engineer for Electronic Warfare Systems at MDA where he focusses on developing EW technologies and teams to support a variety of projects in the land, air, sea, and space domains.

Kyle Davidson

Top
With over 13,000 members internationally, the Association of Old Crows is an organization for individuals who have common interests in Electronic Warfare (EW), Electromagnetic Spectrum Management Operations, Cyber Electromagnetic Activities (CEMA), Information Operations (IO), and other information related capabilities. The Association of Old Crows provides a means of connecting members and organizations nationally and internationally across government, defense, industry, and academia to promote the exchange of ideas and information, and provides a platform to recognize advances and contributions in these fields.