Intelligent monitoring and control systems

The course presents artificial intelligence techniques for designing, training and effectively deploying intelligent systems for a wide range of industrial applications. After the description of standards and certifications of the legislation in industrial monitoring, design techniques for intelligent systems are presented, ranging from the collection of training data to the main learning methods. The course also presents a number of use cases, tools and environments for their implementation.

Main topics covered:

- Legislation in the field of industrial monitoring: standards and certifications
- Description of intelligent systems for industrial automation, robotics, power distribution grids, automotive, and transport systems
- Artificial intelligence: current trends, applications, and major challenges
- Artificial Intelligence for industrial processes
- Data augmentation and transfer learning techniques
- Explainable methods for industrial applications
- Generative modeling for control systems and industrial scenarios
- Image quality assessment (IQA) techniques
- Detection and segmentation
- Defect analysis of products and production lines and anomaly detection
- Self-supervised Learning (SSL) for intelligent systems
- Attention networks and memory for anomaly detection
- Continual learning for control and optimization
- Predictive maintenance for industrial components
- Federated learning and graph-based methods for control systems
- Unimodal and multimodal learning, and information fusion
- Optimization and memory efficiency for edge computing


A detailed list of topics of each lesson is presented and regularly updated on the course site.