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课程大纲
Overview of AI in Defense Applications
- Autonomous systems, UAVs, and real-time surveillance
- AI use cases in defense: navigation, tracking, 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
要求
- An understanding of deep learning and computer vision architectures
- Experience with AI model training and evaluation using frameworks like TensorFlow or PyTorch
- Knowledge of defense-grade system requirements and security protocols
Audience
- Defense AI engineers
- Military tech developers
- Autonomous systems and surveillance platform architects
14 小时
