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Course Outline
Overview of AI in Defense Applications
- Autonomous systems, UAVs, and real-time surveillance.
- AI use cases in defense: navigation, tracking, and reconnaissance.
- Overview of AI model adaptation in mission-critical environments.
Preparing Data for Fine-Tuning
- Working with sensor data: lidar, radar, thermal, and video feeds.
- Labeling strategies for object detection and target recognition.
- Data augmentation and anonymization in military contexts.
Fine-Tuning AI Models for Perception and Control
- Vision models for real-time object detection and segmentation.
- Fusion models for combining multi-sensor inputs.
- Policy tuning for autonomous navigation and obstacle avoidance.
Security, Safety, and Redundancy in AI Models
- Building resilient models with adversarial defense techniques.
- Fail-safe design and anomaly detection during inference.
- Securing model pipelines against tampering and spoofing.
Testing and Simulation in Defense Environments
- Using synthetic data and digital twins for validation.
- Stress testing under adversarial and extreme conditions.
- Sim-to-real transfer in operational simulations.
Compliance and Defense Standards
- AI assurance frameworks for defense deployments.
- Security and ethics in autonomous defense applications.
- Documenting compliance with operational and legal mandates.
Deployment and Monitoring in the Field
- On-device inference and edge AI optimization.
- Telemetry, feedback loops, and continual model updates.
- Case studies from real-world defense AI systems.
Summary and Next Steps
Requirements
- A solid understanding of deep learning and computer vision architectures.
- Practical experience in training and evaluating AI models using frameworks such as TensorFlow or PyTorch.
- Knowledge of defense-grade system requirements and security protocols.
Target Audience
- Defense AI engineers.
- Military technology developers.
- Architects specializing in autonomous systems and surveillance platforms.
14 Hours
